diff --git a/4_bit_LLM_Quantization_with_GPTQ.ipynb b/4_bit_LLM_Quantization_with_GPTQ.ipynb
deleted file mode 100644
index f2dec8d..0000000
--- a/4_bit_LLM_Quantization_with_GPTQ.ipynb
+++ /dev/null
@@ -1,252 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4",
-      "authorship_tag": "ABX9TyOS2QEuJ1BDI/3IFsLsFIZo",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/4_bit_LLM_Quantization_with_GPTQ.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 4-bit LLM Quantization with GPTQ\n",
-        "> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
-        "\n",
-        "Companion notebook to execute the code from the following article: https://mlabonne.github.io/blog/4bit_quantization/"
-      ],
-      "metadata": {
-        "id": "yezrHxYvg_wR"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BhufqqQAaz6e"
-      },
-      "outputs": [],
-      "source": [
-        "!BUILD_CUDA_EXT=0 pip install -q auto-gptq transformers"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import random\n",
-        "\n",
-        "from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig\n",
-        "from datasets import load_dataset\n",
-        "import torch\n",
-        "from transformers import AutoTokenizer\n",
-        "\n",
-        "\n",
-        "# Define base model and output directory\n",
-        "model_id = \"gpt2\"\n",
-        "out_dir = model_id + \"-GPTQ\""
-      ],
-      "metadata": {
-        "id": "dg8NyBL0ZNyw"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Load quantize config, model and tokenizer\n",
-        "quantize_config = BaseQuantizeConfig(\n",
-        "    bits=4,\n",
-        "    group_size=128,\n",
-        "    damp_percent=0.01,\n",
-        "    desc_act=False,\n",
-        ")\n",
-        "model = AutoGPTQForCausalLM.from_pretrained(model_id, quantize_config)\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
-      ],
-      "metadata": {
-        "id": "C9352jN0ZP6I"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Load data and tokenize examples\n",
-        "n_samples = 1024\n",
-        "data = load_dataset(\"allenai/c4\", data_files=\"en/c4-train.00001-of-01024.json.gz\", split=f\"train[:{n_samples*5}]\")\n",
-        "tokenized_data = tokenizer(\"\\n\\n\".join(data['text']), return_tensors='pt')\n",
-        "\n",
-        "# Format tokenized examples\n",
-        "examples_ids = []\n",
-        "for _ in range(n_samples):\n",
-        "    i = random.randint(0, tokenized_data.input_ids.shape[1] - tokenizer.model_max_length - 1)\n",
-        "    j = i + tokenizer.model_max_length\n",
-        "    input_ids = tokenized_data.input_ids[:, i:j]\n",
-        "    attention_mask = torch.ones_like(input_ids)\n",
-        "    examples_ids.append({'input_ids': input_ids, 'attention_mask': attention_mask})"
-      ],
-      "metadata": {
-        "id": "6wuBLe6aZSe-",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "e4ebd71a-2854-4347-cebe-08cf040d1eb6"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/allenai___json/allenai--c4-6e494e9c0ee1404e/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)\n",
-            "Token indices sequence length is longer than the specified maximum sequence length for this model (2441065 > 1024). Running this sequence through the model will result in indexing errors\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "%%time\n",
-        "\n",
-        "# Quantize with GPTQ\n",
-        "model.quantize(\n",
-        "    examples_ids,\n",
-        "    batch_size=1,\n",
-        "    use_triton=True,\n",
-        ")\n",
-        "\n",
-        "# Save model and tokenizer\n",
-        "model.save_quantized(out_dir, use_safetensors=True)\n",
-        "tokenizer.save_pretrained(out_dir)"
-      ],
-      "metadata": {
-        "id": "ETsG2iYrXaUg",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "e48b825e-0ebc-4a73-dbfd-b5571cafd24e"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "CPU times: user 4min 35s, sys: 3.49 s, total: 4min 39s\n",
-            "Wall time: 5min 8s\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "('gpt2-GPTQ/tokenizer_config.json',\n",
-              " 'gpt2-GPTQ/special_tokens_map.json',\n",
-              " 'gpt2-GPTQ/vocab.json',\n",
-              " 'gpt2-GPTQ/merges.txt',\n",
-              " 'gpt2-GPTQ/added_tokens.json',\n",
-              " 'gpt2-GPTQ/tokenizer.json')"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
-        "\n",
-        "# Reload model and tokenizer\n",
-        "model = AutoGPTQForCausalLM.from_quantized(\n",
-        "    out_dir,\n",
-        "    device=device,\n",
-        "    use_triton=True,\n",
-        "    use_safetensors=True,\n",
-        ")\n",
-        "tokenizer = AutoTokenizer.from_pretrained(out_dir)"
-      ],
-      "metadata": {
-        "id": "nktu1FsdZ9sd",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "9943c829-1b58-474a-f245-6aefa09d81dc"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "WARNING:accelerate.utils.modeling:The safetensors archive passed at gpt2-GPTQ/gptq_model-4bit-128g.safetensors does not contain metadata. Make sure to save your model with the `save_pretrained` method. Defaulting to 'pt' metadata.\n",
-            "WARNING:auto_gptq.modeling._base:GPT2GPTQForCausalLM hasn't fused attention module yet, will skip inject fused attention.\n",
-            "WARNING:auto_gptq.modeling._base:GPT2GPTQForCausalLM hasn't fused mlp module yet, will skip inject fused mlp.\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from transformers import pipeline\n",
-        "\n",
-        "generator = pipeline('text-generation', model=model, tokenizer=tokenizer)\n",
-        "result = generator(\"I have a dream\", do_sample=True, max_length=50)[0]['generated_text']\n",
-        "print(result)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cRhIGrXdiFdt",
-        "outputId": "6dca2078-6f01-44da-9895-3a03bdfb4b5b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "The model 'GPT2GPTQForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MusicgenForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n",
-            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "I have a dream,\" she told CNN last week. \"I have this dream of helping my mother find her own. But, to tell that for the first time, now that I'm seeing my mother now, just knowing how wonderful it is that\n"
-          ]
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/Decoding_Strategies_in_Large_Language Models.ipynb b/Decoding_Strategies_in_Large_Language Models.ipynb
deleted file mode 100644
index f628ad7..0000000
--- a/Decoding_Strategies_in_Large_Language Models.ipynb
+++ /dev/null
@@ -1,2885 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/mlabonne/how-to-data-science/blob/main/Decoding_Strategies_in_Large_Language%C2%A0Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Decoding Strategies in Large Language Models\n",
-        "\n",
-        "> A Guide to Text Generation From Beam Search to Nucleus Sampling\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
-        "\n",
-        "Companion notebook to execute the code from the following article: https://mlabonne.github.io/blog/decoding/"
-      ],
-      "metadata": {
-        "id": "qaLKx40NbTD6"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cBr2gYVBVDka"
-      },
-      "outputs": [],
-      "source": [
-        "%%capture\n",
-        "\n",
-        "# Install transformers and graphviz\n",
-        "!sudo apt-get install graphviz graphviz-dev\n",
-        "!pip install transformers pygraphviz\n",
-        "\n",
-        "# Make sure we're using UTF-8 as encoding\n",
-        "import locale\n",
-        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
-        "\n",
-        "# Set seed\n",
-        "import torch\n",
-        "torch.manual_seed(42)\n",
-        "torch.cuda.manual_seed(42)\n",
-        "torch.cuda.manual_seed_all(42)\n",
-        "torch.backends.cudnn.deterministic = True\n",
-        "torch.backends.cudnn.benchmark = False"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## 📚 Background"
-      ],
-      "metadata": {
-        "id": "7R4IKg4lbNcN"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 229,
-          "referenced_widgets": [
-            "9713d401d53341e38141920ea03dbd5a",
-            "7a4520eef6b046049ac015596f45e765",
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-            "8367416a0fae4c46887b947b4627c9ed",
-            "3d8a0c7938274453989b93a819dfbaae",
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-            "abdd1746c797421688fff22da1c24471",
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-            "d30b4657252f44cc9f18dfce198fa490",
-            "64efc321afdd4b3782765a02888a772b",
-            "17831ee3579f4013a5381400fd70b38d",
-            "9a35e7be906641fdbf6170cd73a15fa6",
-            "5a78d9f4fa474d68a1dea3999bb961da",
-            "ddb595c5abd54c81a6589063a0e9c472",
-            "9ae75f0fed3846298fa003bc700cb30c",
-            "974039b5318043569c2e5f04ab321efe",
-            "d44421e59afd4cafa21cde90bd16bd1a",
-            "b3dc8bf4a0814f2ba6ece0544d6ffba1",
-            "6b27b86bcd4f440da024f952cde0fb86",
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-            "b00326a362f24a098ddee078671e2efe",
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-            "ce38696b9d844577ba0a930e50b65f26",
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-            "cae537a671ec4796b414e67987e40108",
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-            "9db081d87e174740b247f80a48065170",
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-        "id": "1LS5sCUPwzaD",
-        "outputId": "147addb9-bbf7-4eeb-bfc0-3b46b06b076d"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "9713d401d53341e38141920ea03dbd5a"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading pytorch_model.bin:   0%|          | 0.00/548M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "64efc321afdd4b3782765a02888a772b"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "5443950734244bb58cc0d16f3a8e6431"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "a53f23ee62874333a78984b0043f04c0"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "85b0326d03b94a2a81a4af035d011cad"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
-            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Generated text: I have a dream of being a doctor.\n"
-          ]
-        }
-      ],
-      "source": [
-        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
-        "import torch\n",
-        "\n",
-        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
-        "model = GPT2LMHeadModel.from_pretrained('gpt2').to(device)\n",
-        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
-        "model.eval()\n",
-        "\n",
-        "text = \"I have a dream\"\n",
-        "input_ids = tokenizer.encode(text, return_tensors='pt').to(device)\n",
-        "\n",
-        "outputs = model.generate(input_ids, max_length=len(input_ids.squeeze())+5)\n",
-        "generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
-        "print(f\"Generated text: {generated_text}\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## 🏃‍♂️ Greedy Search"
-      ],
-      "metadata": {
-        "id": "xw7mElwjbPYW"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "zm_AiUhK8yrP",
-        "outputId": "74bef07c-cc93-4643-d67c-5ba67dfed8eb"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Generated text: I have a dream of being a doctor.\n"
-          ]
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import networkx as nx\n",
-        "import numpy as np\n",
-        "import time\n",
-        "\n",
-        "def get_log_prob(logits, token_id):\n",
-        "    # Compute the softmax of the logits\n",
-        "    probabilities = torch.nn.functional.softmax(logits, dim=-1)\n",
-        "    log_probabilities = torch.log(probabilities)\n",
-        "    \n",
-        "    # Get the log probability of the token\n",
-        "    token_log_probability = log_probabilities[token_id].item()\n",
-        "    return token_log_probability\n",
-        "\n",
-        "def greedy_search(input_ids, node, length=5):\n",
-        "    if length == 0:\n",
-        "        return input_ids\n",
-        "\n",
-        "    outputs = model(input_ids)\n",
-        "    predictions = outputs.logits\n",
-        "\n",
-        "    # Get the predicted next sub-word (here we use top-k search)\n",
-        "    logits = predictions[0, -1, :]\n",
-        "    token_id = torch.argmax(logits).unsqueeze(0)\n",
-        "\n",
-        "    # Compute the score of the predicted token\n",
-        "    token_score = get_log_prob(logits, token_id)\n",
-        "\n",
-        "    # Add the predicted token to the list of input ids\n",
-        "    new_input_ids = torch.cat([input_ids, token_id.unsqueeze(0)], dim=-1)\n",
-        "\n",
-        "    # Add node and edge to graph\n",
-        "    next_token = tokenizer.decode(token_id, skip_special_tokens=True)\n",
-        "    current_node = list(graph.successors(node))[0]\n",
-        "    graph.nodes[current_node]['tokenscore'] = np.exp(token_score) * 100\n",
-        "    graph.nodes[current_node]['token'] = next_token + f\"_{length}\"\n",
-        "\n",
-        "    # Recursive call\n",
-        "    input_ids = greedy_search(new_input_ids, current_node, length-1)\n",
-        "    \n",
-        "    return input_ids\n",
-        "\n",
-        "# Parameters\n",
-        "length = 5\n",
-        "beams = 1\n",
-        "\n",
-        "# Create a balanced tree with height 'length'\n",
-        "graph = nx.balanced_tree(1, length, create_using=nx.DiGraph())\n",
-        "\n",
-        "# Add 'tokenscore', 'cumscore', and 'token' attributes to each node\n",
-        "for node in graph.nodes:\n",
-        "    graph.nodes[node]['tokenscore'] = 100\n",
-        "    graph.nodes[node]['token'] = text\n",
-        "\n",
-        "# Start generating text\n",
-        "output_ids = greedy_search(input_ids, 0, length=length)\n",
-        "output = tokenizer.decode(output_ids.squeeze().tolist(), skip_special_tokens=True)\n",
-        "print(f\"Generated text: {output}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "NeMNyl5maqTc",
-        "outputId": "0d4a5d12-0c05-416f-e5d7-99721b7f7214"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 3633.75x2100 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import networkx as nx\n",
-        "import matplotlib.colors as mcolors\n",
-        "from matplotlib.colors import LinearSegmentedColormap\n",
-        "\n",
-        "def plot_graph(graph, length, beams, score):\n",
-        "    fig, ax = plt.subplots(figsize=(3+1.2*beams**length, max(5, 2+length)), dpi=300, facecolor='white')\n",
-        "\n",
-        "    # Create positions for each node\n",
-        "    pos = nx.nx_agraph.graphviz_layout(graph, prog=\"dot\")\n",
-        "\n",
-        "    # Normalize the colors along the range of token scores\n",
-        "    if score == 'token':\n",
-        "        scores = [data['tokenscore'] for _, data in graph.nodes(data=True) if data['token'] is not None]\n",
-        "    elif score == 'sequence':\n",
-        "        scores = [data['sequencescore'] for _, data in graph.nodes(data=True) if data['token'] is not None]\n",
-        "    vmin = min(scores)\n",
-        "    vmax = max(scores)\n",
-        "    norm = mcolors.Normalize(vmin=vmin, vmax=vmax)\n",
-        "    cmap = LinearSegmentedColormap.from_list('rg', [\"r\", \"y\", \"g\"], N=256) \n",
-        "\n",
-        "    # Draw the nodes\n",
-        "    nx.draw_networkx_nodes(graph, pos, node_size=2000, node_shape='o', alpha=1, linewidths=4, \n",
-        "                          node_color=scores, cmap=cmap)\n",
-        "\n",
-        "    # Draw the edges\n",
-        "    nx.draw_networkx_edges(graph, pos)\n",
-        "\n",
-        "    # Draw the labels\n",
-        "    if score == 'token':\n",
-        "        labels = {node: data['token'].split('_')[0] + f\"\\n{data['tokenscore']:.2f}%\" for node, data in graph.nodes(data=True) if data['token'] is not None}\n",
-        "    elif score == 'sequence':\n",
-        "        labels = {node: data['token'].split('_')[0] + f\"\\n{data['sequencescore']:.2f}\" for node, data in graph.nodes(data=True) if data['token'] is not None}\n",
-        "    nx.draw_networkx_labels(graph, pos, labels=labels, font_size=10)\n",
-        "    plt.box(False)\n",
-        "\n",
-        "    # Add a colorbar\n",
-        "    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
-        "    sm.set_array([])\n",
-        "    if score == 'token':\n",
-        "        fig.colorbar(sm, ax=ax, orientation='vertical', pad=0, label='Token probability (%)')\n",
-        "    elif score == 'sequence':\n",
-        "        fig.colorbar(sm, ax=ax, orientation='vertical', pad=0, label='Sequence score')\n",
-        "    plt.show()\n",
-        "\n",
-        "# Plot graph\n",
-        "plot_graph(graph, length, 1.5, 'token')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "J16z-Mw295Jx"
-      },
-      "source": [
-        "## ⚖️ Beam Search"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 49,
-          "referenced_widgets": [
-            "0d17978b46374b9ba937cbd1f23cc8a6",
-            "b27f6d1a93ca40a2a2444a3e577de8ad",
-            "167368b8456f46dba33398b8772dff77",
-            "79436fe2590443cdac2d6bdb9d1fcc41",
-            "a511af9eb3294b198914e93b9a25abd5",
-            "ad49b293543841bfa47b063f0f309f72",
-            "a53474be60f9448587413ff42a83ebdb",
-            "e910d8d0679b404f87b71cc17a60b50d",
-            "c5d544e4d2714f9d8d2aa9c840e41b4c",
-            "c4989e8977b2418690d0072e6453644e",
-            "51e4c1493e564978b312e66e558dff0b"
-          ]
-        },
-        "id": "DTDkrVdutCk1",
-        "outputId": "2a1e6009-b541-4dd6-ecb2-0c3f07bf0947"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "  0%|          | 0/63 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "0d17978b46374b9ba937cbd1f23cc8a6"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "from tqdm.notebook import tqdm\n",
-        "\n",
-        "def greedy_sampling(logits, beams):\n",
-        "    return torch.topk(logits, beams).indices\n",
-        "    \n",
-        "def beam_search(input_ids, node, bar, length, beams, sampling, temperature=0.1):\n",
-        "    if length == 0:\n",
-        "        return None\n",
-        "\n",
-        "    outputs = model(input_ids)\n",
-        "    predictions = outputs.logits\n",
-        "\n",
-        "    # Get the predicted next sub-word (here we use top-k search)\n",
-        "    logits = predictions[0, -1, :]\n",
-        "\n",
-        "    if sampling == 'greedy':\n",
-        "        top_token_ids = greedy_sampling(logits, beams)\n",
-        "    elif sampling == 'top_k':\n",
-        "        top_token_ids = top_k_sampling(logits, temperature, 20, beams)\n",
-        "    elif sampling == 'nucleus':\n",
-        "        top_token_ids = nucleus_sampling(logits, temperature, 0.5, beams)\n",
-        "\n",
-        "    for j, token_id in enumerate(top_token_ids):\n",
-        "        bar.update(1)\n",
-        "\n",
-        "        # Compute the score of the predicted token\n",
-        "        token_score = get_log_prob(logits, token_id)\n",
-        "        cumulative_score = graph.nodes[node]['cumscore'] + token_score\n",
-        "\n",
-        "        # Add the predicted token to the list of input ids\n",
-        "        new_input_ids = torch.cat([input_ids, token_id.unsqueeze(0).unsqueeze(0)], dim=-1)\n",
-        "\n",
-        "        # Add node and edge to graph\n",
-        "        token = tokenizer.decode(token_id, skip_special_tokens=True)\n",
-        "        current_node = list(graph.successors(node))[j]\n",
-        "        graph.nodes[current_node]['tokenscore'] = np.exp(token_score) * 100\n",
-        "        graph.nodes[current_node]['cumscore'] = cumulative_score\n",
-        "        graph.nodes[current_node]['sequencescore'] = 1/(len(new_input_ids.squeeze())) * cumulative_score\n",
-        "        graph.nodes[current_node]['token'] = token + f\"_{length}_{j}\"\n",
-        "\n",
-        "        # Recursive call\n",
-        "        beam_search(new_input_ids, current_node, bar, length-1, beams, sampling, 1)\n",
-        "\n",
-        "# Parameters\n",
-        "length = 5\n",
-        "beams = 2\n",
-        "\n",
-        "# Create a balanced tree with height 'length' and branching factor 'k'\n",
-        "graph = nx.balanced_tree(beams, length, create_using=nx.DiGraph())\n",
-        "bar = tqdm(total=len(graph.nodes))\n",
-        "\n",
-        "# Add 'tokenscore', 'cumscore', and 'token' attributes to each node\n",
-        "for node in graph.nodes:\n",
-        "    graph.nodes[node]['tokenscore'] = 100\n",
-        "    graph.nodes[node]['cumscore'] = 0\n",
-        "    graph.nodes[node]['sequencescore'] = 0\n",
-        "    graph.nodes[node]['token'] = text\n",
-        "\n",
-        "# Start generating text\n",
-        "beam_search(input_ids, 0, bar, length, beams, 'greedy', 1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "xusUHHBa4lt6",
-        "outputId": "a6689abb-cf16-4cb9-b20b-e17c75db992e"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Generated text: I have a dream. I have a dream\n"
-          ]
-        }
-      ],
-      "source": [
-        "def get_best_sequence(G):\n",
-        "    # Create a list of leaf nodes\n",
-        "    leaf_nodes = [node for node in G.nodes() if G.out_degree(node)==0]\n",
-        "\n",
-        "    # Get the leaf node with the highest cumscore\n",
-        "    max_score_node = None\n",
-        "    max_score = float('-inf')\n",
-        "    for node in leaf_nodes:\n",
-        "        if G.nodes[node]['sequencescore'] > max_score:\n",
-        "            max_score = G.nodes[node]['sequencescore']\n",
-        "            max_score_node = node\n",
-        "\n",
-        "    # Retrieve the sequence of nodes from this leaf node to the root node in a list\n",
-        "    path = nx.shortest_path(G, source=0, target=max_score_node)\n",
-        "\n",
-        "    # Return the string of token attributes of this sequence\n",
-        "    sequence = \"\".join([G.nodes[node]['token'].split('_')[0] for node in path])\n",
-        "    \n",
-        "    return sequence, max_score\n",
-        "\n",
-        "sequence, max_score = get_best_sequence(graph)\n",
-        "print(f\"Generated text: {sequence}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 362
-        },
-        "id": "3gPbd4dw0J9k",
-        "outputId": "1733ac6d-ce76-4344-d3c4-e84bffb1be5e"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 12420x2100 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "# Plot graph\n",
-        "plot_graph(graph, length, beams, 'sequence')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0vmObi_P6bR0"
-      },
-      "source": [
-        "## 🎲 Top-k sampling"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "xmsDJWQs-tav"
-      },
-      "outputs": [],
-      "source": [
-        "def plot_prob_distribution(probabilities, next_tokens, sampling, potential_nb, total_nb=50):\n",
-        "    # Get top k tokens\n",
-        "    top_k_prob, top_k_indices = torch.topk(probabilities, total_nb)\n",
-        "    top_k_tokens = [tokenizer.decode([idx]) for idx in top_k_indices.tolist()]\n",
-        "\n",
-        "    # Get next tokens and their probabilities\n",
-        "    next_tokens_list = [tokenizer.decode([idx]) for idx in next_tokens.tolist()]\n",
-        "    next_token_prob = probabilities[next_tokens].tolist()\n",
-        "\n",
-        "    # Create figure\n",
-        "    plt.figure(figsize=(0.4*total_nb, 5), dpi=300, facecolor='white')\n",
-        "    plt.rc('axes', axisbelow=True)\n",
-        "    plt.grid(axis='y', linestyle='-', alpha=0.5)\n",
-        "    if potential_nb < total_nb:\n",
-        "        plt.axvline(x=potential_nb-0.5, ls=':', color='grey', label='Sampled tokens')\n",
-        "    plt.bar(top_k_tokens, top_k_prob.tolist(), color='blue')\n",
-        "    plt.bar(next_tokens_list, next_token_prob, color='red', label='Selected tokens')\n",
-        "    plt.xticks(rotation=45, ha='right', va='top')\n",
-        "    plt.gca().spines['top'].set_visible(False)\n",
-        "    plt.gca().spines['right'].set_visible(False)\n",
-        "    if sampling == 'top_k':\n",
-        "        plt.title('Probability distribution of predicted tokens with top-k sampling')\n",
-        "    elif sampling == 'nucleus':\n",
-        "        plt.title('Probability distribution of predicted tokens with nucleus sampling')\n",
-        "    plt.legend()\n",
-        "    plt.savefig(f'{sampling}_{time.time()}.png', dpi=300)\n",
-        "    plt.close()\n",
-        "\n",
-        "def top_k_sampling(logits, temperature, top_k, beams, plot=True):\n",
-        "    assert top_k >= 1\n",
-        "    assert beams <= top_k\n",
-        "\n",
-        "    indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]\n",
-        "    new_logits = torch.clone(logits)\n",
-        "    new_logits[indices_to_remove] = float('-inf')\n",
-        "\n",
-        "    # Convert logits to probabilities\n",
-        "    probabilities = torch.nn.functional.softmax(new_logits / temperature, dim=-1)\n",
-        "\n",
-        "    # Sample n tokens from the resulting distribution\n",
-        "    next_tokens = torch.multinomial(probabilities, beams)\n",
-        "\n",
-        "    # Plot distribution\n",
-        "    if plot:\n",
-        "        total_prob = torch.nn.functional.softmax(logits / temperature, dim=-1)\n",
-        "        plot_prob_distribution(total_prob, next_tokens, 'top_k', top_k)\n",
-        "\n",
-        "    return next_tokens\n",
-        "\n",
-        "# Start generating text\n",
-        "beam_search(input_ids, 0, bar, length, beams, 'top_k', 1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "sUrEPE899TsK",
-        "outputId": "da72aba4-398d-4d73-b28c-298bec2fae4c"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Generated text: I have a dream: to be able to\n"
-          ]
-        }
-      ],
-      "source": [
-        "sequence, max_score = get_best_sequence(graph)\n",
-        "print(f\"Generated text: {sequence}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 363
-        },
-        "id": "qyop3NTp--4G",
-        "outputId": "3347ffa8-7f1a-43b0-f611-f872b741ae0e"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 12420x2100 with 2 Axes>"
-            ],
-            "image/png": 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t194OzyzpuV0yPvMB+J/T4+W2Nrj0J3DnQz3vO39WYvnpXvzOHex2bPHfVZIkSZKkzBCNtrNx68KgY2Sc7GyYNDNeXv4qvPwiNNTHyqEwTNun631nzI0/ful5WPJcp+fnpDar+m6jN3Ud0hrW+/ffUU42zJ0YL7+4Ap56FbY3xsrhEOw/uet9D5waf/zEK/DfTgvoHThlz+cuyIUZY+Llxma4uxdfXbc8nlie301OJafe94YkSZIkSZI6qN02tK8XRaOw+IXk269fF9t2KRsGVV3ME9taG39cUgpFxckdv70dFiU5p2xmp76sF56NLV7R37Ky4HNfgSOPidc1NsA3vwrPP9P/5x8otduedqELKYM0rh7a32dBmttpcc/HezlNqXP7uSP3Lo8StTdto2Xz0qBjSJIkSZJSzHvMBavzpePW1uT3ffBfqc2iRM4hkSQp0ZYhPiZkqDj+5PjjpkZ4+H747yOwvS5ef/gCyMsb+Gzpqql5HY1Nq4OOIUmSJEmSJElSgnDQASRJkiRJkjT4bXLyTb977N/Jt320U9sps7puN3u/xPL9t/cuk3rW1t7ElrpFQceQJClt7Nji70aAvNz448am3u3b8aYELb24IcF/noL1m3p3rj352HvhQ2fHy23t8I2fw209/G49eF5i+ZZ7e3djhcHM94ckSZIkKVNs27GUltZtQcfISDPmxB+3tcKtf42Xx02EgqJu9psbf/z6q/Dsf+PlrCyYPDO1OdV39p0PXdH2Nho2DKIV1lLkgCnxx61t8Ks74uXpY6C4oOv9Dpwaf7x4JTzQYSHA7AjMm9Tzuc88JLH8y39BfRLX5Fdvhr8+mFj3jkN73k/dq1/vZ6MkSZIkSZLial3oggfu7UXbexLLM/fpul3HMca9GZv75GOwaWNybYdVwLgJ8XLdNnioF3Pe9kYkAp/6EhxzYryuqQm+/TVY+OTAZOhvLnQhZZbGVX6fBWXuqFif4S4L34AVm5Pf/8bnEssHjE1NLsU1rvb9IUmSJEmDzQbHyQeqtDyx/Nri5PZ74Sl47r89t1Pfbdy6kGi0PegYkiSljS2OCRkSDj0KCjvMh773jtjW0XEnDWymTOD7Q5IkSZIkSZKUbsJBB5AkSZIkSdLgt3HrU0FHGPSefhSWv9Jzu221cPfNiXWHH991230PhuqaeHnZy3D/v/qaUN3x/SFJUtyOLU68ASjpMGlr63bY0ZD8vhVl8cd1O2D5mz3vU98IP/1z8udI1vnnwoXvipfbo3DFr+DGu7vfZ/pEmD8rXl63CX51beqzZaLtW/zdKEmSJEnKDBu9aWWfTZ+TWH61ww0np8/tfr+J0yAnN/a4rS3Wr7fLhGmQm5e6jNo7G+wbHLKaal+mvbU+6Bhp58CpieVnlnX/XEdzxkNeTuxxazu88Hr8udnjIT+n53OftD9UFMfL67fCl/4ALXtY9K+uHj77G2hsjtdNH73nrOpZ89bXaG2qDTqGJEmSJEmS0kA0GmXLNq+lP/YfePXlntvVboFbb0isO/bErtuWdVj4bHsdvLGi5+M3NMAff91zu45OOSOxfPVvYfOm3h2jr8Jh+Pjn4IRT4nXNzfCdS+GJxwYmQ3/z/SFljsbVjp0ISkkenDg9Xo4C3/t3cvvetQSeXRUvF+TAKbO6b6++aVzl95kkSZIkDSYNTRvZ3pDERWf1m2FVUFYRL694FZ76z573Wf4K/OGH/ZtL0Ny6jW07lgYdQ5KktGGf99CQmwtHHRcvL38NFj0XL0+YDBOnDHyudLdlm33MkiRJkiRJkpQptm3bxvXXX8+nPvUpjj/+eObPn8/kyZOZNGkSjz02SCYzAuGgA0iSJEmSJGlwa2ndQe32l4KOMehFo/CrK6F2DzeDa26CX3wbGnbE60aOgVn7dt0+HIEzzkusu/Y38NBdvcu2dhVc/TPYMkA3qss0LgAmSVLcji1+LwJMHBN/HI3CA/9Nft99piWWf3YNtLd3376xCb78fVi1rncZk/WBd8Cn3hcvR6Pwvd/Ctbd3v8/574RwKF6+5lb43T9i+yZr/Sb46Z9h8Wu9z5yu6rcsJBrdw1+mJEmSJElpYpN9H302fjLkF3b93Iy53e+XlQ1TZvZ+Pw28HQ0raWzeGHQMBaB+vZ+NXZk5Foryu37uoKnd75edBftO7P1+HeXnwNfelVj3yGI47wfw+EuJ19ZbWuHeZ+Gd34VXOix2lB2By96b3Pm0Zw3rFwYdQZIkSZIkSWmgoelNmlu8jh6Nwg++CZv3MBerqQm+eznUd5gnNmYczJvfdfvpnfqSrv7tnscYNzXCdy+DdWuSzw2xxStGjIyX67bBZZfAxg3J7b9pI6x6o3fn7CgUgo9+Gk45I17X2gLf/wY8+lDfj5sual3oQsoYjat8vwbp/Qcmzku57xX45cN73mfJOvjavxLrzp4LRbmpzzfU+f6QJEmSpMFl41bHgKaDgxcklv/4I7jzemioT6zfshFuuQZ+8GWo3w7ZOVBRPXA5h6INW13kXZIkgGg0Su02fzsOFcef0v1zx500cDkyyZZt/m6UJEmSJEmSpHS3ZcsWPvvZzzJmzBje9a538fOf/5x///vfPPvssyxbtozXX3+dhoaGLvd917veRSQSIRKJMGHChAFO3jfhoANIkiRJkiRpcNu64xWgF6sUq9fKKyASgY3r4fLPwGP3Q0tz/Pn2dnhxIXzzs/Daknh9KAT/84nYf7tz4JFw1Nvi5bZWuPpn8P2vwnNPxG5k11lrK7yxHO65Ba68GL52ETx0V2xf7W7r9peCjiBJUtpoqFvSc6Mh4MA5ieXv/gZ+8Hu4+2F4/Fl44vn4tvzNxLYnHpF4g8ZHF8IXr9y9XVMz/Ptx+J8vxo4DMH5Uyl8KAO9+O3z+Q4m/O3/8J7jmlq7bz50OF747se6318GHvgz3PALbtu++T1s7vP4m3HwvfOZb8I5PwF9vg8am1L2OoLW11tHS2Ms7SEuSJEmSFIDa7V7j6atwBKbO2r0+KxumzNy9vqPpc3pXr+DU2j84JDVu9rOxK5Ew7Ddp9/qcLJg3cc/7HjClm/qpyZ//qH3gIycm1r2yCi76BSz4CrzrSjj3O7Dgy/DF38OazfF24RB89Z0wtZ+urQ81TVv8bJQkSZIkSRLU7fBa6rDK2Dyx9evgCx+DB++F5k7zxJ55Cr70CXhpUbw+FIKPfqb7eWJHHgvhDneaevq/8O2vwxsrEts1N8OjD8HnPgrP7VxzZPTY5PNnZ8PnvxL77y5vrIDPXAD/+GvsdXW2Yzs88Sj86Aq46P3wcgr+N/jwx+CMc+Ll1lb44bfhoX/v/bGDtL3ea6lSJmhvaaC1dkXPDdVvZtfA+w9MrPvJQ/DpG+CV9Yn1Wxvg94/De6+Gug5zUMaWw6eP6v+sQ1HTBr/PJEmSJGkwcQ5Jejj+TCivjJdbW+HmP8Pn3wuXfwK+8wX43/PhKx+GO66D1pZYu3PPh2HVwWQeKmq3vxx0BEmS0kJzy2aaWjYEHUMDZPRYmLHP7vW5uXD4goHPkwnqdvi7UZIkSZIkSZLS2aOPPsq8efP4yU9+Ql1dHdFo79Zk/OIXv0g0GiUajbJy5Uruu+++fkqaOllBB5AkSZIkSdLgVt+4KugIg15VDRx1Etx8DWyrhd/9EK75BVQOj93sbuN6qO9igejT39vzIkkA774wdhO5px6O1730fGyLRGIT1wqLoL0N6ndA7abYxDclZ4fvEUmS3tLc4PciwGHzYexIWLk6Vm5ugX/cGds6O/ko+NrH4+Xxo+CM4+HGu+N1jz4T24ZXQEU5NDTCmg3Q2OGmjPNmwElHwhW/6p/XdPbbIDsLvvsbaN/ZD//zv8Re24fO3r39+86ALVvh2tvjdS8tg6//OLag3PBKKC2O1dftgE21ia9nsGpuWEVOvivpSZIkSZLSm30fe2f6HHjuicS6SdMhJ3fP+82Yu3tdTi5MnJa6bEoN+9CHppYd/r1358Ap8NCLiXVzxkNeTg/7TQNuS6zLy4F9xvfu/B8/BcoK4Me3QktbvH5bfWzrSlEeXPpeOLaLz171je8RSZIkSZIkATR6DZ2akXDiKfC3P0HtFvjxd+FXP4HqERDJgg3rYHvd7vu9630wY1b3xx09Fk44Be7scF114ROxrbIKyodBYyOsXwtNHcbkztwHjjoOfvmj5F/DpKnwqS/BT74LLTsXT6vfAX/9Y2wbVgllZRAKwdatsGkD9PI+X0l53/mQlQ3/+Gus3N4ey9TaCseckPrzDYQG3yNSRmjdtjroCAI+cxS8tA4eez1ed/fLsa26CKqLYUcTvFkLLe2J+5blww/PhIIe+izVN2071hFtayUU8TaYkiRJkjQYOIckPRQWw0VfhZ9dFrsP3S7tbbB65e7tQ2E464NwxInwxIMDFnNI8j0iSVJMQ5PfiUPN8SfDkhcS6w49CgoKg8mT7hqaVhGNRgmFQkFHkSRJkiRJkiR18tRTT3HCCSfQ0NCQUB8KhaioqGDDhg09HmP+/Pnst99+LFy4EIB//OMfHHvssf2SN1XCQQeQJEmSJEnS4FbfuCboCEPC298Jx54aLzc1wqoVsHIZ1G9PbBsKw6nvju2TjKws+OjFcNYHIKfTjXra2mDDGnj91di5Nq6L3QCus6ISyPYmP11q8D0iSRIAba31tLVsDTpGWsiKwBWfh1HD+7b/Zz4Ah+23e/26TbD4NVj+JjR2uCnx/Fnw3S9BJNK38yXr9OPgqx+DcId5Vb+5Dn51bdftP/1++NrHobjTRLX2KKzZAC8ti22r1iW+nl0K8qFokE1ya27wt6MkSZIkKf01NPnv170xY+7uddPn9Lzf2ElQUJRYN2VmrK9P6cU+9KGpdYd/7905cNrudQdM7Xm/GaOhpCCxbt+JkN2Ha93vXQA3fhVO2R8KcrtvV1YI7zoSbvkaHNvF57X6rsX3iCRJkiRJkoDGZq8TAZzzXjjljHi5sRFWvg7LX4PtdYltw2E497zYPj354Edh/4N2r9+4AV59Gd5YAU0dxuTOngtfvqxvY4wPOwouvRIqKnd/bvNGWPYaLH0VNq6HaLT3x0/Wez4A735/vNzeDj//Adzzr/47Z3/yPSJlhta61UFHEJCTBVe9E06dvftz67fDi2tg+WZoaU98btww+Mv/wKwRA5NzSIpGad2+LugUkiRJkqQUcXx8+hg7Cb78Izjo6Fj/QVdCIZgxDy7+Hhx3+kCmG7rqG71eKEkSQKNzj4ecg4+A/E7z/449KZgsmaC9vZGW1tqgY0iSJEmSJGmgRd36tEkDaPv27Zx22mnU19cDEI1GOfjgg7nlllvYtm0b69bF5keEQqE9HQaAs846661j3H333f0XOkW8lakkSZIkSZL6VX2Tk24GyrsvgKmz4eZrYM0bXbeZNAPO+SBMntH74590Fhx6DNx9Ezz5MGzesOf2peWxxZn2OxTmHODCSt1padtOc2sdOVnFQUeRJClQLd7QIMHEMXDN9+G+x+CRhbB0BWzeCg2N0Na+532zs+C7X4Lr7oBrboFNtV23q6mC95wK7zih+xsXpNrJR8XyXfYzaGuL1f3xRmhugU/+T9ftjzwArr8D7nwIVvbwv0lxIey/DxyxPyw4CPL2sEhdJmr2pgaSJEmSpDTX3t5KQ9P6oGNktNHj4be39X6/cBh+8re9O/eVv0u+7WHHxbb+OPZg540rh6aWHf69d2fKSHjmJ73fLxyGB7+TuhyjK+Gb74OWVnhuOazaBFu2QzgE5cUwrgpmj+v99fTLz4ttfbH/lL792WQi3yOSJEmSJEkCaHQe2ls+/DGYNQf++kd4c2XXbabNhPefD9NnJXfM7Gy45DK4/Wa46Tqo3dx1u+rhcPo5cOLb926M8YzZ8PM/wp23wj3/glVvdt82KxtmzoYFx8OhR/b9nF05572QnQNX/yZWjkbhqh9DSwucnGELuzU2rSYajSZ1IzRJwWmtc35MusiJwHdPg7Pnwi8fhqfegNZu5uSMLYd37wfv2T+2n/pXa91qsktHBR1DkiRJkpQCO7y2n1bKK+CDn4Nzz4dXF8Gm9dDUADl5UDkcJk6HkrLEfT7/7UCiDhn13k9HkiTAMSF74/q7+rbfrLl93/fc/4lte2PTBmhsiJfHjIuNdVH3GpvWkJNdHnQMSZIkSZIkSVIH3/ve91i7du1bc/o++clP8qMf/YhwHyZfHnfccXz1q18F4PXXX2ft2rWMGDEipXlTyeUGJUmSJEmS1K8anHTTL7pb8Gj+obHtzdfh9ddg2xaIZEFZeWzSWdVeXqssLYdzPhTb1q2GN5ZD3Vao3w7hCBQUwLAqqBkTm+im5DQ0riGnqDjoGJIkBarZ3427ycuFU46Obb0VDsO7ToGz3wZLlsJrK2BrHUTCMKwMpo6HKeMT9+nNuWqq4bHrep8L4PjDYluyigrgg2fFtvWbYclrsGVb7PWEQ1BYAFXDYPwoGDV8726ynO5aGnyfSJIkSZLSW0PTOiAadAwprdU3eY1nKGqp9+89U2Rnwf5TYpsGTssO3yOSJEmSJEmK3cB/KLrx7q7rDz48tq1YDq+9ArWbISsbyofBtBkwvKb35wqH4dR3wMmnw2svw+vLoK4uVl9eDuMnwYRJifscc0Js64ucHDjt7Ni2fl3snFtrYcf22GspLoaRY2DCRMjL3/OxZs/t/s+qJ2ecE9syXXt7Ey2ttS50IaW51m0u3JQqo8pgyVf2/jgHjott2xrh6Tdgw3aobYD8bKgohBnDYUJF749738f3PttQ1Vo3NH/3SZIkSdJgVO+9QtJSYTHMOzjoFALY4ULvkiQB0DBEx4QMZf++E6Idppwfe1JwWTJFQ9NqSopmBh1DkiRJkiRJktTBr371K0KhEADHHHMMP/7xj/t8rDlz5hCJRGhrawNg8eLFjBixlwsK9qOsoANIkiRJkiRpcKt30k0gRo+Pbf1p+MjYpr1X37ia0qKpQceQJClQLQ3+buwPWRHYZ2psGwyqh0H1gUGnCE5zo+8TSZIkSVJ6s29Q6lm913iGnPbWRtoaNwcdQ0prLTv8bJQkSZIkSVLsBv7a3bgJsS2VIhGYNjO2DZTq4bFNe6exaTU52eVBx5C0B611LtyUrkryYMGUoFMIoHWbv/skSZIkabBwfLy0Zw1N62lvbyUcdkkQSdLQ1uiYkCGltRX+fXe8nJMLRx4bXJ5M0dhkX7MkSZIkSZIkpZOFCxeyfv16AEKhEJdffvleHS83N5fRo0ezYsUKAJYvX77XGftTOOgAkiRJkiRJGtzqGx04KfWk3gHGkiTR7O9GqUctDb5PJEmSJEnpzb5BqWcN9g0OOS31a4OOIKW9tsaNtLc1Bx1DkiRJkiRJAWvyGrrUIxe6kNJfa50LN0k9aa3z+0ySJEmSBoPWtgaaW7cGHUNKc1EamtYFHUKSpMDZ1z20PHgPbN0SLx92NBSXBBYnYzQ02dcsSZIkSZIkSelk8eLFbz0uLy/nkEMO2etjlpWVvfV469b0HncTDjqAJEmSJEmSBrd6BxhLPfJ9IkkStDT4fSj1pNkFZCVJkiRJaa7BPg+pR/YNDj2tO/w7l5LRWu9NfSVJkiRJkoayaDTqQhdSEnyfSOmvtc73qdQT3yeSJEmSNDjUe/8DKSnOI5EkyfnHQ0ntFvjbn+LlUAhOOTO4PJnEMSGSJEmSJEmSlF7Wr18PQCgUYty4cSk5Zl5e3luPm5qaUnLM/pIVdABJkiRJkiQNXu3RNhqa1gYdQ0p79Y2rg44gSVLgmv0+lHrU0uD7RJIkSZKU3uzzkHrW3FJLa1sDWZH8oKNogLTs8LNRSkbLjtXkFI8JOoYkSZIkSZIC0tyymfZoc9AxpLTX2GS/g5TuWrf5PpV60lrn+0SSJEmSBoMdziGRkrKjcTVVQYeQJClg9nUPXs8vjP23qQneeB3uuBW2bok/f/ARMG5CINEyju8TSZIkSZKkISgKoWjQITKMf14aQG1tbW89jkQiKTnmli3xi+hlZWUpOWZ/yQo6gCRJkiRJkgavpuaNRKNtPTeUhriGxjVBR5AkKXAtDX4fSj1paVpPtL2VUNiufkmSJElSeqpv8hqPlIz6xjWUFE4MOoYGSMsOPxulZPhekSRJkiRJGtqamr0+JCWj0feKlPZa63yfSj3xfSJJkiRJg4NzSKTk1HuPOUnSEBeNRmn0t+Og9Y0vd/9cQSG8/8KBy5LpGnyfSJIkSZIkSVJaqa6uBmJ9HevWrdvr4zU3N7NixYq3ypWVlXt9zP4UDjqAJEmSJEmSBq/mlq1BR5AyQlNrbdARJEkKXKu/HaUkRGlrrQs6hCRJkiRJ3bJ/UEpOs/2DQ0pbs5+NUjLammuDjiBJkiRJkqQANbfUBh1ByggtvlektNfeZP+g1JP2xtqgI0iSJEmSUsBr+1JynEMiSRrq2tsbaY82Bx1DAywvH77wdahI73Xb0kqLvxslSZIkSZIkKa2MGjXqrcdvvvkm69ev36vjPfLIIzQ1Nb1VnjVr1l4dr7+Fgw4gSZIkSZKkwas92hJ0BCkjRKOtQUeQJClwUX87Sknxt6MkSZIkKZ3ZPyglJ9ruNZ6hJNruZ6OUFD8bJUmSJEmShjTHR0rJ8b0ipbdoextEo0HHkNKe4yYkSZIkaXBo93qllJR2r4VIkoY4fzcOHdnZUDMa3nYa/OAq2Gde0Ikyi2NCJEmSJEmSJCm9HHbYYeTm5hIKhQD4y1/+slfH+9nPfvbW4+rqambOnLlXx+tvWUEHkCRJkiRJ0uDlAGMpOU5MkyTJm9ZJyfK9IkmSJElKZ95URUqOfelDjNf0pKR4/VuSJEmSJGlos59JSo79TFJ6s79DSlKb7xVJkiRJGgy8ti8lx2v7kqShzt+Ng9v1dwWdYPDwvSJJkiRJkiRJ6SU/P58FCxZw5513AnDllVfyvve9j4qKil4f67bbbuPmm28mFAoBcMYZZ6Qyar8IBx1AkiRJkiRJg5c36ZGS0x5tCTqCJEmBc8KNlJyovx0lSZIkSWms3f5BKSn2Dw4tjp2QkhNt97NRkiRJkiRpKPPauZQcx91Lac6+QSkp9g1KkiRJ0uDQ7r/vpKR4bV+SNNT5u1FKjvP0JUmSJEmSJCn9fPWrXwUgFAqxYcMGTjvtNGpra3t1jDvuuIPzzjsPgGg0SlZWFhdffHGqo6ZcOOgAkiRJkiRJGrzanWwjJcWJaZIkAX4fSklxoURJkiRJUjqzf1BKjv2DQ4t/31JyvP4tSZIkSZI0tHktVUqO7xUpvdnfISXH94okSZIkDQ7OIZGS43tFkjTU2c8tJcf3iiRJkiRJ0hAUBdrderVF+/QnLfXZYYcdxtlnn000Gvuf7/HHH2f27Nn87ne/Y/v27d3u19bWxqOPPsq73/1uTjvtNOrq6ohGo4RCIT71qU8xfvz4AXoFfZcVdABJkiRJkiQNZl7tlZIRjbYHHUGSpMDt6qyV1BN/O0qSJEmS0pnXeKRk2D84xHj9W0qOn42SJEmSJElDm9dSpaTYzySlOb/PpOT4fSZJkiRJg4TXQqRkeG1fkiR/N0rJiHp/OUmSJEmSJElKS3/84x9ZtmwZCxcuJBQKsXr1ai644AI+9rGPMXXqVCC23k4oFOLTn/400WiUFStWUF9fn/BcNBrl6KOP5sorrwzy5SQtHHQASZIkSZIkDV6hUFbQEaSMEA5nBx1BkqTAhcL+dpSSEQr521GSJEmSlL7C9g9KSQl7jWdI8fq3lJxQxM9GSZIkSZKkocx5aFJynIcmpTf7BqXkhPw+kyRJkqRBwWv7UnK8ti9JGur83Sglx7nHkiRJkiRJkpSeCgoKuOOOOzj66KOJRqOEQiGi0SgtLS0sWrTorXbRaJTFixezePFiduzYQTQaBXir/cknn8xNN91EOBwO6qX0SmaklCRJkiRJUkZycSMpOQ4wliTJyWlSsrzBoyRJkiQpnXmNR0qON64cWlzgSEqO178lSZIkSZKGNq+dS8mxT1ZKc/YNSkkJRfztJ0mSJEmDgffNkpLj/RglSUOdY0Kk5DgmRJIkSZIkSZLSV1VVFffddx9XXHEF5eXlb9WHQqGErWMdQDQapbS0lCuuuILbbruNkpKSQPL3RTjoAJIkSZIkSRq8HDQpJcf3iiRJfh9KyfK9IkmSJElKZ96MT0qO13iGGP++paT42ShJkiRJkjS0eX1ISo7vFSm9hcK+R6Wk+F6RJEmSpEEh7L/vpKQ430qSNNTZzy0lx9+NkiRJkiRJkpTeQqEQF198MW+88Qa//OUvOfXUUykvLycaje625eXlcdxxx/H973+f119/nYsvvphQKBT0S+gVr1pLkiRJkiSp3zgxLTnr18DyV2DLJmhtgcIiGDEaJs+A7Jxgs9VugqUvwdZaqN8OuXlQOTyWrbg0dedpaYbXlsDGdVC3FUJhKC2HMRNi22DnAGNJkrzBY6baUQ+vr4a1G2DTFmhoitUXF8KwMpg2AWqqAo046PhekSRJkiSlM//d2jP7BqGtDVa9Dm+ugB110NQIeflQVBLrG6wZA+Fw6s6XjuwfHFr8bFQyWtvghdfhjY2wZTu0tUN5EUwdCTOGwOci4AJHkiRJkiRJQ5wLXWS2aBSWvQrLl8b6mgDKymH8RJg4BVJ1T662NnhlCaxZDdtqob0dSkph3ESYNGVoXEv1vSKluXAk6ARKkbZ2WLQWlm6ETTugtR0KsmFECUysgImVEM6se06mFfvRJUmSJGlw8Hplzzasgdd3zSFphYIiGDEKJqXJHJJlL8O2LVC/A3JzoWJ4LFsq55DI94okSX4XZr5oFJa9Bq8vjY3XgNg9g8dNhImTUzcuZKjzvSJJkiRJkiRJmSE/P58LL7yQCy+8EIB169axadMmamtrKSgooLKykhEjRpCVldnXfTM7vSRJkiRJktJaVqQw6Ahp7ZnH4La/w8qlXT+fmw+HHQunvmtgJ4K1t8OTD8GdN8Iby7tuEwrBzHnw9nfBlJl9P9fqlXDrX+G5J6Glues2ldVw3Omw4BSIDNL7PmVFCoKOIElS4ML+duzS+s2w+DVY/Grsv0uWQX1D/PkRVXDTzwcuT2MT3P4APLsEFr0Kazb0vE9NFZyyAM5+G5QWpT7T48/CZ7+9e/3P/w/2m5X68wUtHMkPOoIkSZIkSd3K9hpPt+wbhHWr4e6b4b8PQGND9+0Ki+HQY+D402FYVd/Pl87sHxxawtl+NnZlfS28uAJeWAGLVsDilbCjKf58zTD416VBpeteQzO88zvwxsbE+lMPhMvP6/3x1myG39wFdy9MfP0dVRTDOYfD+46F/IBvctyfwll+NkqSJEmSJA1lEa+dd2nTRnj1JXj15dh/l74KDfXx56uGw6/+HFy+1lb4502xbfPGrttUVMLb3wGnnAF9vVfXhvVw/V/gkQcTX39HZeVw4tvhjHMgN69v58kE9jNJ6S0UChHKLiDa0s2H1RC2rg6eXx3bXlgNL66BHR3mFY8shfs+Hly+Xd6shd89Dv9aDNsau29XlAsHjYNz5sFRk1Nz7r8vhEvv3L3+3o/BqLLUnCNdhLL9PpMkSZKkwcDrld179nH4Vw9zSA45Bt7+bigqGbhc7e3w1H/g7hvhzT3MIZkxD05+J0zu5RySR++Dq3/c93xTZsPnu7h/SKbzvSJJGuoi4VwgDLQHHSXtbNoIr+0cE/Lay7Csi3Ehv7g6uHytrfCvm+D2m/c8LuSUM+GkM5IfF3L/3fCLH6QqJfz8T1A9InXHC4rjpyRJkiRJkoaeUDS2KXn+eSkdDR8+nOHDhwcdI+X6OB1UkiRJkiRJ6ll+7uC7oJYKLS3wx5/EFvXZk6YG+Pc/4cn/wEWXwNTZ/Z9tWy384tvw2pI9t4tGYdEzsPhZOOFMOOv9EA737lz//Dvc8leI9jD+fON6uPY38Oi/4eNfgYrq3p0nExTk1QQdQZKkwGXnDYJZQyny3Evwt3/Coldh45ag0yTasAW+/7ve7bNmA/z2OrjhLvjSR+Dog1KXp74Rrvx16o6X7iLZZYQj+UHHkCRJkiSpW/m5XuPpzL7BmHtugRuvhpbmntvuqIu1f/geePeFcOgxvTtXJsi3f3BIyS7ws3GXZ5fBn/8NL6yADVuDTtM3P/8nvNHNjdp66+bH4Irrobl1z+021cFVd8A/n4Afng9TRqbm/Okmu9DPRkmSJEmSpKEsL8frQ7ssWQS3/iO2sMXmTUGn6d7G9XDFpbD8tT2327QR/vRrePh+uOSy2KIXvXHfnfDrn8b63fakdgv8/c/wwL1wyaUwbkLvzpMpfK9I6S+reAQtm5cFHSMtLHwD/vBfeH41rN8edJo9a4/Cbx+Dn/8Hmtt6br+9Ce57BXIicNTkvT//2m3w/fv3/jiZIqvY7zNJkiRJGgwKcv33XWctLfDnn8ATD+65XVMDPHA7PP0wXHAxTBmgOSS/ugKWJjGHZPEzsORZOO4MOLMPc0iUyHvMSZKGulAoQl5ONY3Na4OOkhZeWgS33RAbF7IlzceFfPey5MaFXP0bePgB+NKlvR8XkgpZ2QN/zv6Q57+xJEmSJEmSJElpxOEikiRJkiRJ6jeRcA55OQGMOk1j7e3wqyt3X9woHIbK4TBmIuQXJj5XtxX+36Ww9KX+zbZ1C3zr87svbhQKQVUNjJ8Sy9hRNAp33Qh/+WXvznX1z+DmayDanlhfVAJjJ8HIsZCTk/jcyqXwva9A7ebenSsTFOQN0pVJJEnqhZx8vw93WbIUHnwCNm4JOknyigth/CiYNQWmjIPy0t3bbNkKX/0h3P5A6s77i7/A2hQtMpcJfJ9IkiRJktKdfR6J7BuM+eff4e+/hZbmxPqsrFi/4MRpMGLU7jfDbKiHP/w/eOiu3p0v3UXC+eRkdXEBTYNWVqGfjbssWgH/fh42bA06Sd+8uAL+1sONiJP1u7vhsr9Bc2tifXE+TBsFU0ZCYW7ic29ugvN/AkvXpCZDusn2vSJJkiRJkjSk5eZU462QYl57Gf77CGxO4wUuarfA17+0+wIXObkwZhyM7mJu2NJX4etfhG29uEZ8w9/g5z+MLRbXUWERTJgE4yZCfkHic+vWwNe+AG+sSP48mSQv12upUrrLKvZ9ussLa+DeV2D99qCT7FlLG3z+JvjRA9DclvhccS5MrIA5I2FSBeT308JJl90J25v659jpKKvE94kkSZIkDQaFziFJ0N4Ov/0uPNFpvPFbc0gmdD2H5KeXwbIBmEPynS/A0q7mkIyAcd3MIbnnJvjbVf2bbSjwvSJJkn3dHS19GZ54BLak8biQrbVw6R7GhYwaC9mdxoUsexUu+1LvxoWkwoTJMKxiYM/ZX/J9n0iSJEmSJEmS0khW0AEkSZIkSZI0uOXn1tDYPIRWI+7BXTfCs/9NrDvqJDj1nVC2c7Bse3uszbW/gc0bYnXNTXDVlXDZz6Cg0+S1VGhrix1/0/p4XTgMx58Ox58BZcPi9Zs3wB3/gAfuiE1OA3jwTpgwDQ4/rudzPXjH7gsUTZoB53wQJk2PTYaD2Gt+8mG44Y+wrTZWt3Ed/Pp78KUr+vhC01R+bk3QESRJClxOnt+HySjIg/rGoFPAxDFwyL4wdzrMmgzDynZvs3o9/PN++Ott0LRzYcv2KFz5a5g1BcaP2rsMz70EN94dexwOxRbLbG7Z8z6ZLtv3iSRJkiQpzRXY55HAvkFY/grc8pfEuvJKOPsDsN+hkN1hUZCGenj0Prj5mthjiJ3zb7+G6XOgepD871WQV0NoV6eohoTswkHyP28/K8iF+jRezKelFS77a+w6N0B+DjQ09+1YDy+Gn9+eWDepBj57Ohw8HSI713dsaYOHF8GPboY3dg692VoPX/gdXHsx5PbTwkpByfK9IkmSJEmSNKSFw1nk5lTT1Lw26ChpLS8fGhuCTgE/+z6sXR0v5+TAeR+G40+C3LxYXWMD3PMv+MsfoHnn9dQ1q+BnP4CvXN7zORY+AX/9Y2LdmHHw/gtg7n4QicTqWltjbf/463im7XXw3cvgB1fFsg0mefbJSmkvq9j3aTIKcqC+j/1tqfa/t8OdHRYYzQrDufvCGfvA7Jr4vGeI9Re+vgkeXhbbJxXd//9cBA/sXDiqIBvqB/ncGPB9IkmSJEmDRYH3P0hwz03wXKc5JEe+DU7uNIfkuf/C9b9NnEPym+/C138K+f00h+Q3V8LmTnNIjj0djjsdSjvNIbnrBniowxyS/9wJE6bCoUnMIens+DNh5r7Jty8o6v05MoHzrSRJ2tnXXRd0ivSXLuNCfv59WLcmXs7Ogfd+CI7rOC6kEe79F/z1D9DSYVzIL34Il1y25+PPmw9f68N9haNR+N7l0NThPnxHH9/746Qrx4RIkiRJkiRJ0uBQV1dHfX09FRUVZGVlBR2nzzI3uSRJkiRJkjJCQV4NW+peCDpGWti+DW6/LrHuHe+Dk89JrAuHYb9DYpO9rvwSbNw5YWzLRrjnZjj9vanP9tj98OqieDkUhgu+BPsftnvbYVXw3otg7CT400/j9Tf+CQ44PD4QuSv1O+Aff0qs2+8QuPDi+A3ndsnJhcOOhWmz4buXwOadC3m88iI89UjX2TKVE9MkSYLsfL8POyvIh+kTYcYkmDk59t816+HjPUzq6k+VZXDdT2DMiJ7bjqyGC94JRx8En7gM6nbE6lta4bfXwTc/2/cczS3w7aviN0s460T4z9OwdkPfj5kJcrz5hyRJkiQpzeX7b9e32DcYc9u18Ws4AJXV8OXvQ2n57m3zC+DYU2H6HPjOl6ChPlbf0gx33gDv+0TyrzGd2Tc49GQX+nfeWWEuzBgDs8bBrLGx/67eBOf/tOd9g/K7u+G1nTdsqy6FE/aDa+7v/XHa2uHK6xM/G+dOgF98DApyE9tmR2DBHNh3Elz4M3hlVaz+9fXw94fgfcf27bWko3B2MZHsfrhbsyRJkiRJkjJKXm4NTc1rg46RNvILYNIUmDwVJk+LbevXwte/GGyuZ5+ChU/Gy1lZ8LVvw6w5ie3y8uHUs2DiFLjsEmhtjdU/9Ti88CzsM6/7c7S1wW9+nngtddpM+PoVkJ+f2DYrCw48FGbMhv/7Ery+LFa/6k341y1wRqf+uUznQhdS+ssqGRl0hLRTmAOzRsDskTCnJvbfVbXw/r8EnQxufTG27VJdBL9+F0yr7rp9OAQTK2Pb+w6ErXu5+NSWevj23fHyJ4+EK+/bu2Nmgqxiv88kSZIkaTDIySolEs6jrb2x58aD3PZtcEenOSRnvA/ednZiXTgM+x4C46fC9y+GTR3mkNx7C5z6ntRn++/98NrieDkUhg9/EeZ3M4fk3R+FsRPhzz+L1998NcxPYg5JZzVjYca8PsUeVAqcbyVJEvn2de8mvwAmToZJO8eETJ4aGxdy6ZeCzfXc0/BMh3EhkSz432/DzH0S2+XlwdvfARMmwze+DG07x4U8/Ti8+CzMntf9OcorYltfsjV1+OdHJAsOX9D746Sr/Fz7miVJkiRJkiQpE0WjUa699lquueYaHn74YbZv3/7WcyNGjODEE0/kfe97H0cffXRwIfsgHHQASZIkSZIkDW4FDpx8y503QmOHm9hMnQUnnd19+/IKeP+nEuvuuSU2yS3V7vhHYvnYt3e9uFFHR5wAhxwTL2+rjeXbkwf+BQ074uXySvjQZyAS6X6fyuHwoU4LY9/8Z2hv2/O5MklBnu8TSZJy/D58y+Hz4a8/hHv+AD//P/jEeXDMwVBTFXQyyM+DMSN6t8/U8XBRpxssPLoQmpr7nuN318PK1bHH1RVw4bv7fqxMkp3v+0SSJEmSlN7sG4yzbzB2A6HFzybWnf1BKC3f836jxu3+Z/Xck123zUT2DQ494exiwtmFQcdIC0fOhn98GR66En7zKfjM6XD8vjByWNDJ9mzpGvj9PfHyJedAYW7fjnX3M/Dmpni5IBe+84HYf7tTVhhrk91hbMUf7oXte7mQUjrJLvSzUZIkSZIkSZBnXxMABxwMP/4N/PlGuPx78L7z4dAjoXp40Mli/vanxPKZ74RZc7pvP2sOnHFuYt1f/7jnczz6EKxbEy/n5cPnvwL5+d3vU1wCn/sKZGXH6276O9Tv6H6fTOT7REp/WcUu3LTLgilw2/nwxOfhT+fBF4+BE2fAqNKgk8VsqYfvdOgHLM6Fq8+DadXJH6N0D99NyfjW3bBlZ7/frBHwPwfs3fEyRVax32eSJEmSNBiEQiEKHR8PwN2d5pBMmQUnntV9+/IK+J9PJtbd109zSO66IbG84BSY38McksNOgIM7LCK9rRbuuzXl0YaEnOwysiJ7eRFJkqRBwL7uuPkHww9/DX+8AS79HvzPR+CQI6AqTcaFXNtpXMgZ58LMfbpv39W4kM7HSJX7704szz8IStKk7zEV8nLta5YkSZIkSZKkgRaNRrntttu49dZbufXWW7n99tt7tf/y5cuZP38+5513HnfeeSd1dXVEo9G3tjVr1vCnP/2JY489lnPPPZft27f30ytJvXDQASRJkiRJkjS4FeQ5cBKgvR0euTex7rT3QCi05/1mzI1NYtulsQGefDi12da8AetWxcuRCLztHcnte8q5ia/hP3d33xbg2f8mlo89FfIKej7P9DkwaUa8vHYVvLIouYyZwPeJJEmQne/34S6jR8CE0RAeRL25JxwG4Q6/GxuaYN3Gvh3rldfhL7fFy1/4MBQOkXn+Of5ulCRJkiSlOfs8YuwbjNm0AVpbOpwrC+YkuUhH5xtpbt0MTY3J7Zvu8r350JATCoXIKvDvHWBMFUyqyazr3+3tcNlfoaUtVj5mDizYwyJ+PXnghcTy2w+AEeU97zdhOBw3L16u3QH3Pdf3HOkmu9D3iCRJkiRJkryB/y4jRsKYcel5LXXFcnj15Xg5Lw/OOKfn/c48N9Z2l5cXw5sru2//xKOJ5aOPg8okFqIfPRYOOTxertsGj6W4vy1YYXJzqoIOIakHWcUu3LTL2HKYXJU4nySdXPUIbOmwKOlnjoZxwwbu/Pe/Crcvjj2OhODykyGSht///SGr2N99kiRJkjRYFHhtn/Z2eOy+xLq3v7vnOSTT58LkmfFyYwM8neJr2mvfTJxDEo7ACUnOITmp0xySh3uYQ6KuFbrQuyRJgGNCOkr3cSGvdRgXkpsHpycxLuT0c2Jtd+lpXEhf7NgBTz6WWHf08ak9R9Dy/O0oSZIkSZIkSQPuySef5PTTT+fMM8/kzDPP5He/+13S+65cuZJDDz2U5557jmg0SjQaJRQK7bbteu6GG27gxBNPZMeOHf34ilInDbsyJEmSJEmSNJgUOHASgKVLoG5rvFw1Aqbtk9y+h3caTPvs46nLBfDKi4nlcZOhrCK5fUeMguEd/oo3rYfXX+u6bVMjrOj03LwDk885t9NCSAsf67pdpgmHssnNTvIPXJKkQSwnz9+Ng1lhAZSVJNbV1vX+OK1t8O1fQtvOReYWHARH7L/3+TJFTr7vE0mSJElSesuK5JOTXRZ0jMDZNxizo9P1n6ISyMlN7lzDulizqSEz5mj0qMBroUNSdqF/75nqLw/ACytij4vy4OIkbta2Jws7fW4eleT3A8CRsxPL/35u77KkE98jkiRJkiRJAsh3Hlrae+LRxPKhR0F+Qc/75RfAIUcm1v33ke7bL3ohsXzAIcnlAzjg4E7nSfFCcUHKyx1BKBQJOoakHmQVu3BTJmhuhVs6jKGoLIR37jtw59/eBJffGS+/70CYOWLgzh+0rBJ/90mSJEnSYOH4eFj2UuIcksoRMDXJMcKHdZpD8tx/U5cL9m4OyfBRUN3hr3fz+t3vIaeeFeR5vVCSJHBMSKZ4qtN9fg89shfjQo5IrHvy0a7b9tVjD0JzU7xcWgb79eKexpkgP9ffjpIkSZIkSUNOu1ufNimF7rjjDgCi0SgAn/3sZ5PaLxqNctZZZ7Fu3ToAQqEQoVCIaDS629bxuccff5yLL764f15MioWDDiBJkiRJkqTBLd9JNwA8/1RieeY8CIWS23fWvMTyyy9AU2MqUsVs2pBYHjOhd/uP7tS+u8lztZvji1QDZOfEJrclq3OuZ1M8SS8o+Xk1hJL9n0GSpEEskl1KKJwXdAz1o+aWxHJxYe+P8dfb4OXlscdFBfC5D+19rkyS7b+vJEmSJEkZoMAbq9g3uFNBp+s/LU1dt+tK57ahMBQWJ79/OvPGlUNTdqF/75nozY3wi3/Fy588FapL+368ljbYuC2xbmovxk10bvvfV6Cxue950kmW7xFJkiRJkiQBeTleJ0p3Tz+RWJ63X/L7zu3U9qlu+plaW2Pz0DoaPzH584yflFh+/hlo6kU/VTrzPSJlhqwSF27KBPe8DFsb4uWTZ0JkAO/K+P1/w9q62ONRpfDJIwfu3Okgq9jvNEmSJEkaLBwfDy88mVieMS/5OSQz5iWWX0nxHJLNneaQdJ4T0pPO7Z9/out26l5BntcLJUkCyHPucUboPC5k7vzk953TaVzI0ym+Z/AD9ySWDz8GIpHUniNIoVCE3JyqoGNIkiRJkiRJ0pDz4IMPvvV40qRJHHHEEUnt98c//pGnn346Yd25cDjMhz70Ie68805eeuklnn76aa666ipmzpxJNBolFAoRjUb51a9+xaJFi1L+WlJtAKfZSJIkSZIkaShy0k3MG8sSy5NmJL9vWQVUVsfLra2wemVqcgHsqEss93bRoKJO7Vcu67rd9k4LeBQUJT9BD6CwJLG8eQNsr+u6bSZxATBJkmJCoRA5+f52HKxWrIbt9fFyQR6M6eXPoJWr4XfXx8sXvQcqy1OTL1Pk5PvbUZIkSZKU/uwftG9wl+EjIScnXq7fAetWJ3ee5a8klkePg+ycrttmGvsHh6bsQj8bM9E3roXG5tjjOePhnMP37nhbd+xeV1qQ/P5lhYnlphZYvm7vMqWL7AI/GyVJkiRJkgR5uV5LTWfRKKzo1Dc0bVby+0/v1HbFstgxO6vbtntd5z6qPenctrkZ3kxhf1uQXAxGygxZxX6fZYIHlyaWDxo3cOd+YgVc90y8/H9vg/zsgTt/0MJ5pYSz84OOIUmSJElKkULnkPDm8sTypOnJ71tWARWd5pCseSM1uaCLOSRFvdu/85yTzvNl1DPnkEiSFJPvmJC0F43uPmd46szk9+88LuT1bsaF9MWaVfDy4sS6Bcen5tjpIjdnOKFQJOgYkiRJkiRJkjSkRKNRnn76aUKhEKFQiLPPPjvpfX/4wx8mHCcSiXDzzTfz29/+lhNOOIGpU6ey7777csEFF7Bw4ULOPvtsojsvnLe3t/P73/8+5a8n1cJBB5AkSZIkSdLgVlIwOegIaWHNm4nlkWN6t3/N2D0fb2+EQonl9vbe7d/WlljubuJcqNPVyF6fp3X3ulRO0gtKSeGUoCNIkpQ28or87ThY/fGGxPIJh0NWL+ZYRaNwxa+guSVWnjMNzhxkE896Egplk1MwgHfTlCRJkiSpj+wftG9wl6xs2PeQxLq7buz5HNEo3Nmp3eGD6FqQ/YNDU26pn42Z5sZH4YlXYo+zIvD1d+/+Gdpb4S72b+vF53Br2+51y9f2PU86yS3zs1GSJEmSJElQaD9TWtuwDpqa4uW8PKiq7r59Z9XDITc3Xm5shI0bdm/X1bXY3vRpde7PAnhzZfL7p7PCAq+lSpkgkl9GOH9Y0DHUgxdXJ5anDY/9t60dHloKn7sJTroK9vseHPgDOPGX8Nkb4cbnoKGl7+dtbIGv3wG71nk6ZSYcManvx8tEORV+n0mSJEnSYOIckt3nfNTs5RyStSm8r9pAziHpTnsbrFsNS5fAspdg3SporO/9cTJVaaHvEUmSAPJya4iEC4KOoT3oPC4kt5fjQqo6jQtp6mZcSF88cE9iecJkGDcxNcdOF0X+20qSJEmSJEmSBtyyZcuoq6sjGo3N8jjppJOS2u/pp59m0aJFhEIhotEooVCIz33uc5xyyildts/OzubPf/4z48ePf2ufv//97yl7Hf0lK+gAkiRJkiRJGtzyc6soyBtFfeOqoKMEprkJNncacFte2btjDOvUfm0KFzgqLE4sb6vt3f51WxPLG9dBSwtkZ3c6T1FiuX47tLZCVpJXKTufB2IT4abMTD5rOqoo3TfoCJIkpY3C8v3Yuu7uoGMohZqa4Rd/hTv/E68rL4Hz39m749x4Nzy7JPY4OwsuuXDvF5nLNPmlswmHs3tuKEmSJElSwCpK9ws6QqDsG0x02nvgmcdjfy4AD90FFdVw8jldX99paYa//gqWPBevGzsJjkpuHkjay8kupyh/XNAxFID86qH92ZhpNmyF/3dLvPyBY2FSzd4ft6SL+/Nt3g4jk1z7anPd7nXL1+1dpnSRX+XYCUmSJEmSJEFRwVQi4Xza2huCjqIurOrUZ1VR1ftjVFYlHmfVG7svlFFcsvt+W2uhenhy59hau3vdqpXJJkxvZcVeS5UyRd7I/ahfem/QMdSNukZ4fXO8HAnBqFJ4Ywt86VZ4tosp8XVNsHIL3PkS/PhB+NwCOH2f3p/7p/+BFTvPXZoPXz6+b68hk+XW+H0mSZIkSYNJ5RC/f1ZzE2zZyzkknduvTeHt+jrPIenqPm57UlebWN7UwxySzu68Hq77NTR26voJhWHUOJgxD44+JTbPZLCqHOLzrCRJ2iUUClNaPJfNWx8LOoq6sbrTuJDKPowLqahKPM7qLsaF9FZ7Ozx0X2LdghP27pjpqKzY342SJEmSJEmSNNCWLl361uNwOMz8+fOT2u+OO+5IKOfk5HDxxRfvcZ/c3Fy+8IUv8IlPfAKANWvWsHr1akaOHNnL1AMnyWUFJUmSJEmSpL6rLN2flY0pnE2VYbZvg2g0Xo5kQUlZ745RVpFY7u0Esj2pGpFYfv3V3u2/4rXEcnt77DWXd8o8rDL22ttaY+W2VnhzOYyfktx5usrV28WY0lFl6f5BR5AkKW0UlifXmav0suhV2NHhRgPNLbBlK7y0DO7/b+zxLhVl8IMvw7DS5I+/biP84q/x8nmnw4TRex074xSV+7tRkiRJkpQZKkuH9jUe+wYTDR8JF3wRrvoOtO7sJ7zpz/DY/XDAEbGbVeblQf0OWLEUnngQtmyK7z9mInz6/yBrkMx8qCydTygUCjqGApBfORdCEYi2BR1FSbjieqjbed17bBV85MTUHDcrAiPKYe2WeN2iFTByWHL7L35j97pNdanJFqSs/Gqyi4bghX9JkiRJkiTtJhzOorR4XzZvfTToKOrC1trEckUvF2+D2PyyVR0Wueh8TIBIJLaAxsYOi8W99jJUD0/uHEtf2b2utovzZKKyEscTS5kib9R86pfeG3QMdeONWugwtIPCXHhtA7znaqhr6nn/9dvhktvgtY3w+QXJn3fRGvjTf+PlLx4DFYXJ7z9Y5I/y+0ySJEmSBpPiggnkZpfT1LKl58aDUFdzSIrLeneMsk5jiVM5h6Sy07X1Fb2cQ7JyaWK5pzkkna1f3XV9tD12/7k3l8N9t8Lhx8PZH4ac3N7lS3fhUDbDimcHHUOSpLQxrHR/Nm99LOgY6kbnMRzD+jguZHWHcSHbUvDbdtFzsHF9vJyVDYf3oo8uU5SXDO35+ZIkSZIkSZIUhJUrV771eNSoURQUFCS13wMPPPDW41AoxIknnkh5eXmP+5155pl84hOfeOt+lM899xwjR47sXegBNEhuASpJkiRJkqR0Vlk6n5Xrbgk6RmAaGxPLObnQ2/VscvMSy02NXbfriymzEsurV8KqFbGFhnryyouJiw/t0tSwe112DoyfAkuXxOueeChW15NoFJ58uIvzpPDPIRghKkr3DTqEJElpo7DciTeZ6Mpfw6sr9twmJxtOORrOfyeUl/Tu+N/9DdTvWmRuJHzgHX2KmfF8f0iSJEmSMkV58WzCoWzaoy1BRwmEfYO7m3cQfOX78JdfxfsK174Jt/2t+31Ky+HYU+H4MyA7u+dsmaKy1Gs8Q1U4K5+8YTNp3PRC0FHUg7sXwv3Px8v/+07ITeHn0L4T4Y6n4+W7FsLxSQ4buPPp3evqk1h8Kd3lV89/ayKeJEmSJEmSVFYyn81bHw06hrrQ2KlPKC+v63Z70rkfrPMxd5kxG/5zf7z88ANw6JHJnaPjfj2dJ5NEIoUUF04NOoakJOWNtG84ndV1GocRAi66Hup29r3lZ8Mps2D/MVCWD7UN8ORKuH0RNLbG9/vtYzC8CM47oOdztrTB/94ObTsXQz1wLJw1NyUvJ+PkjfL9IUmSJEmDSSgUorJ0Pqs23ht0lEB0nu+RkjkkKbymPXkv5pC8+iLU9mEOSW+1t8FDd8bmm3zqMigdltrjB2lYyT5EIrlBx5AkKW2UFdtHkM4GclxIb9x/T2J5/oFQ3Mv72GWC8hLfH5IkSZIkSUNRKBrblDz/vJRK27ZtA2LjX4YNS27AQjQa5YknniAUChGNRgmFQhx33HFJ7VtTU0N1dTXr168nFAqxevXqPmcfCOGgA0iSJEmSJGnwG+oLuHSeqNWXxXmycxLLqbzh2vCRMHJMYt0Nf+p5v/Z2uOnPXT/XeVGnXfY9OLH84F2wcV3P5/rP3bBhze71qVzoKQilhVPJySoOOoYkSWkjt2A8kezyoGMoxbKz4NyT4D2nQnkvJ4zd+RA8+kzscSgEF18AOYNoscveKCwf2v+ukiRJkiRljkgkl/KSfYKOERj7Brs2dhJcfAWc9QHIydlz26JiOO50OOKEvv35pbOh3nc+1BVU+/ef7mp3wJU3xMunHwQHpHgduQVzEssPvAAvvN7zfo8tgSdf3b2+oSklsQLle0OSJEmSJEkdlXkj/7TVuc+qc59WMnI6re/VXT/TQYcllp94FF5Z0vPxn30KXnxu9/pU9rcFpax4X0KhSNAxJCUpb5TfZ+lsW6c+tq2N8GZt7PGsEfDPC+AbJ8Pp+8BRk2P//eYpsfpp1Yn7fu/fsLyLRUA7++1j8NL62OOcCFx60l6/jMwUySZn+OygU0iSJEmSUqyqbOheC+l8D7RUzCFJ5X3Vho+Emk5zSG6+uuf92tvhlmu6fi6ZfGMmwMnnwif+D771W/jxdfDzG+G7f4JPXw7Hnwn5hYn7rFoBP/9m5t9XriPnkEiSlKjcMSFprfMYjj6NC0nx/OiGenji4cS6o0/Yu2Omo0ikkOLCFE9klCRJkiRJkiT1qKEhfiE7Nzd3Dy3jlixZwvbt2xPqDjvssG5a766mpuatx3V1dUnvF4Rw0AEkSZIkSZI0+FUO4YlpAC0tieWsrN4fo/OEtpbmvufpyglnJpaffxL+8cfu27e3w1+uglcXd/18SzeLaxx5IuTlx8tNDfDTb8LWLd2fa/GzcO1vun6uOcMX8XBimiRJiUKhEEXD9g86hlKspRWuuRXe9Rn49lWwI8nJaJu3wv/rsPDm2xfAfjP7JWLaC4WyKSgduovGSpIkSZIyT2Xp0L3GY99g1556BL56EdzwR2ju4fVsr4u1u+Qj8K/rY+cfLCrsHxzS8quH7mdjpvj+DbB55zywYcXw2TNSf45j5sKoini5rR2++HtYsb77fV5eBV/9c9fPNbZ0XZ9J8qv8bJQkSZIkSVKcC12kr859PFl9WcCt0z7dzQ076DAYPiJebm+H730DVr/Z/bGXL4X/d2XXz/XUP5UJynxvSBklu3wC4fzyoGOoG/XdfC+MKIbfvRtGlnb9/Kgy+MN7oLLDwpzNbfCH/+75fEs3wlWPxMsfPQwmVHTffjDLHb4P4azkbggqSZIkScocQ/k+Wp3ne0T6MIek8/X2VM8hOe6MxPILT8JNf+qyKRC7Jn/tVfBad3NI9pBv7ET4yo/gqz+G086D2fOhohpy82J/NiXlMGMenPVB+OZvYO7BifuvfA1u+0syryozVJU5h0CSpI6KC6cTCef33FCB6Pw7LxXzo/d2vMZj/4GmDmNLSsth3wP27pjpqLx4P0KhSNAxJEmSJEmSJGnIKSyMTxDZunVrUvs88cQTCeWcnBz22Sf5dUTy8vLeelxfX5/0fkHoQ1eBJEmSJEmS1Dv5ucMpyBtFfeOqoKMEovPg29bW3h+j8yJJnY+5tw49Bh69D15ZFK+78wZYugSOOx2mzISCIti+LdbmrhthxWuxdgWFUL8j8Xi53YwnLyiEcz8MV/8sXrfqdfi/T8DbzoJ5B0FFVezPaNWKWKaH74kvaNT5XHkZPm69omzoTtqUJKk7heXz2brunqBjqBeu/l5ieUcDbK6Fxa/BXQ/DY8/E6tva4bZ/w5Kl8LOvQ2nxno/7w9/D1l2LzJXCJ85LefSMkV86m3DEmztKkiRJkjJHZel8Xg46REDsG9zd334N993WoX0eHHki7HsIjBwb6/Nr2AErl8IT/4HH74e2NmhqhBuvhjeWw/mfh3CG37cnJ7uc4oIJQcdQgAqq7R9OZw8vhtufipe/cCaUFnbfvq8iYfjKufCJqyAajdWtq4V3fxfOWwDH7wujK4EorFgPdy6Eax+Cpp3fDcX5UNcQP17BILh07HtDkiRJkiRJHRUVxBa6aGtv6LmxBlROTmK5taXrdnuyWz9YTtftIhG44FPwza/Gr6Vu2ghf+BicehYceiSMqIk9t3oVPHw//OuW+KIZhUWwY3v8eB3uB5axyoq9liplklAoRN7I+dQvvTfoKOpCbjd3X/zCMVDawxiI8gL4/AL48j/jdbe+CF85HvK6GN/RHoX/vR2a22LlyZXwkUP6lnswyBvp95kkSZIkDUZVpUP333udr3O39WEOSefr7ameQ3LIMfD4v+HVDnNI7to5h+TY02DSzNh19e3bYm3uvglW7mkOyR6uuY/uxZSJwiK48BL4zZXwzGPx+gfvgGPPgPKK5I+Vrobye0OSpK6Ew1mUFs9j89bHem6sAdf5t206zI9+4O7E8hELYmNKBpuyEn83SpIkSZIkSVIQysvLAYhGoyxfvpz29nbC4fAe93nsscR+jjlz5pCV1c1ElS7U1ta+9Tg/P70X30v+VUmSJEmSJEl7obJ0PisbVwUdo88WP5t825nzEsudF/vpPBg3GS3Nez7m3gpH4IIvwpVfhg1r4vWvLo5t3YlE4EOfhZ99M7G+YA8LgBx5YmzhogfuiNdt3wb/+ENs687hx8cWPXr60Xhdfj8sNDKQKp2YJknSbgrLM/v78Ynnk2974Jz+yxGkwvzYNqYGTjwi9mfyvz+Cup03NHhtBVz+c/jBJd0f4z9PwX0d+u0/8wEoKerX2GmtKMPfF5IkSZKkoSfT+0DsG+xaX/oG//l3uO+2eLm6Bj71fzBiVGK74lKYtV9sO/JE+OnlsL0u9tyT/4m1P/29yb2+dFVZuh+hUCjoGApQfuVcCEUg2hZ0lD55/OXk2x48rf9y9IcdjfCtv8fLh86Ak/bvv/MdOgM++Xb4SYfPx4Zm+M1dsa07h0yHiSPgLw/E64rTe95aj7Lyq8guGh10DEmSJEmSJKWRTF/o4rmFybedu1//5egPeZ37wZq7brcnzU2J5T3dm2vf/eG9H4Jrfheva2yE6/8S27ozbz6MHgv/vCleVzgIxiG70IWUefJGzad+6b1Bx+izR5cn3/bQXixqmQ4KcnavK82HE6Ynt/9JM+Hb90Ddzu+1plZ4fjUcOG73ttc8Cc/unF4fAi4/GbIH4YJMycob5feZJEmSJA1GxQUTyM0up6llS9BR+mTJs8m3nTEvsZybl1hO1zkkH/4C/ODLsGFtvP61xbFtT/u9/zPwy28l1qfyvm/hMJz3SXj5eajfeU+SlmZY+DAce3rqzhOEcCibYcWzg44hSVLaKS+Zn7FjQgCe78W4kDmZNi6k82/bvowL6bRP57EmvbFuDby0KLHu6BP6frx0Vu6YEEmSJEmSJEkKxJQpU9563NzczOOPP86hhx66x33uvvtuQqEQ0WiUUCjEEUcc0atzbty48a3HpaWlvQs8wLKCDiBJkiRJkqShobJ0f1auuzXoGH32w68l3/a3tyWWOw/gbW6CaBR6s65NU2NiufOEt1Qoq4Cvfh9+/b3kFnQqLoUPfgbGTdr9uT0tcARw3segcgTcfA209jBZLxSCY0+Fsz8IP/1G786T3kJUlOwbdAhJktJOYXlmT8D59Dd7brPLY9f1X450cuCc2E0QPvo1aI/G6h5dCE88H3uus+318N3fxMuH7AvHHzYwWdNVYXk/rrInSZIkSVI/KC+eTTiUTXu0D3dtTAP2De6uL32D61bDbX+Ll7Oy4VNfhxGj9nyuSdPhwovhB/8br/vXP+DIt0F5Rc9Z01Vlqdd4hrpwVj55w2bSuOmFoKP0yUU/T77tMz/pvxz94Se3wtqd91rOy4Gvntv/5/zg8VBdBt+5HrY39ticE/eDr70LfnRLYn1xim9uPNDyq/cn1JsvSUmSJEmSJA0JZSX7Z+xCF5ddknzbG+/uvxz9ofOCFI1JXNvsrHM/WOe+tc7e8U6oqIDf/Azq63s+/uFHw0WfgT/9JrG+oKg3KdNPJFJIceG0oGNI6qW8kZk9P+bDf+u5zS5LvtJ/OfpDQfbudfNGQXYkuf1zs2CfkfDo8njdorVw4LjEdqtq4ccPxsvv3A/2Hd3ruINK/ijHTkiSJEnSYBQKhagsnc+qjfcGHaVPfvz15Nte1ek2ep3ne6TzHJKLvw+/+z4sebbn9sWl8L5Pw9g+3F+utwqL4NDj4N4O46QXPwvHnp7a8wy0YSX7EInkBh1DkqS0U16S2X0F3/hy8m2vv6v/cvSHfhkXshdz3x64J/bbepcJk2HchL4fL52Vl2R237IkSZIkSZIkZar99tuPrKws2traAPj1r3/NoYce2m37Rx99lBUrViTcN23BggVJn2/t2rVs2rTprfLYsWP7kHrghIMOIEmSJEmSpKGhquyAoCMEpqgkcSJaWytsq+3dMWo3JZZLyvY2VdeKSuBz34DPXAbzDup6oHDZMHjbWXDpT2HO/rB9W6djFCd3Q7i3vQO+/Ws47jSoGrH781lZsN8h8IVvwbvOj5V3dDpXdU3yry3dlBZNIye7JOgYkiSlndyC8WTlVAYdQym2z1Q48YjEutsf6Lrttf+EjTsXmcvPhS9+pF+jZYTCYZk9YVOSJEmSNPREIrkMK5kbdIxA2DcY98C/YOc8DgAOPQZGJLmIx4y5MGNevNzWCo9k5n1Q31JZ6jUeQcHwoTt2Il2t2gTXPxIvX3QSjKwYmHOfcgD88//gIyfCpC7GP4RDcMh0+OFH4DsfgMI82Lojsc2YqgGJ2m8Kqv1slCRJkiRJ0u7KS7yWmo5KyxLLmzb2/hibO+3T+ZhdOeo4+OXVcPZ7YMy43Z8Ph2HefLjkUvjcVyC/AOo69WnVjOx91nRSVrwfoVAk6BiSeilvtN9n6aqiiwU6xw/r3TEmdGq/acfubX72H6hviT2uLoLPJ38/y0EplJVLzvDZQceQJEmSJPWToXqPua7mkNTV9u4YtZsTy8Wlex2rS0Ul8OnL4ZOXwtxu5pCUDoMT3gFf+wnssz/sqEt8vjDJ+8v11rROU5DWrUr9OQbaUH1PSJLUk2Glfkemq85jODqP8UhGX8aFdCUahQc7zSlecELfjpXusiLFFBdODTqGJEmSJEmSghIF2t16tUX79CctdSkvL4/jjjuOaDRKNBrlmmuu4a677uq2/de+9rWEcmlpKSeckPwF7IcffjihPHVqel8fzgo6gCRJkiRJkoaG6mGHEQ5l0x5tCTrKgMvJhWFVsGl9vG7zBigtT/4YmzYklpNdFKivZu8X29raYrnrtsbqyytjCxyFw/G2a95I3HfclOTPM6wS3nV+bNu6JbY1NsQWcBpWGfuz2yUahTVvJu4/vhfnSjcjK44JOoIkSWkpFApRUr2AzW9eH3QUpdjRB8EdD8XLL7zcdbu6+vjjhiZ4x8f7dr6PX5ZYPvdk+OwH+nasIEWyyyksHZqLxUqSJEmSMltN5TFs3PpU0DEGnH2DcUueSyzPPbB3ueYeAEuejZdfXdS7/dNLiBEVRwUdQmmgaPQxbF78+6BjqIPtDbHxCLv86JbY1lu3PRHbdinKh/9c2fN+pYXw8VNiW+0O2LAVttVDWSHUDIOC3MT2y9YmlmeO6X3WdFI02rETkiRJkiRJ2l3VsCG+MniaGtWpz6pzn1YyNnbaZ9TY5PYrLoH3fCC21W2DzZtgex2UlEJlNeR3WqDtzZWJ5UnpfQ+wHlUN81qqlImyy8aRPWwiLZuXBR1FnYwth+wItLTF64pyeneMok79eNsad29T1xR/vH47HPCD3p1jl+N+kVi+5Dh4fy/HYKSD/LGHEc7K7bmhJEmSJCkjjao8hmdfuyLoGAMuJxfKq2BzxzkkG6GkF3NINg/wHJJZ+8W2trZY7o5zSEo7zyHpdL193OT+yTSsMrG8fWv/nGcgjar02r4kSV0pLpxOXs4IGpvX9txYA2pkp9+hncd4JKPzWJKRfZz7tvh52LAuXs7KhsMH6XCiqmFHEwpFgo4hSZIkSZIkSUPWhRdeyJ133kkoFKK9vZ13vOMdXHrppXzkIx+hvDw2AGTZsmVcfPHF3H///YRCIaLRKKFQiPe+971kZ2cnfa677777rcfl5eWMHZvkBMuAZAUdQJIkSZIkSUNDTlYxNZULWLXh7p4bp6Hf3rZ3+48YnbjA0eo3YEIvbpq2ttMiQv09OW2XSASqa2Jbd958PbE8vo+T00rL97zo08Z10NTh5j/lFb1bJCrdjB1+WtARJElKW+UjT2Xzm9cHHaNPHrsu6ATpa9TwxPKm2kBiZJzymlMIhe3alyRJkiRlnrHDT+WFpd8NOkaf2DfYfZve9A1uXJdYrhzedbvuVI5ILG/Z1Lv908nw8kPJz60KOobSQMm4kyAUgWhbz43TzDM/CTrB4FdWGNu609wCKzvcAC4rDNMG6DuiP0RyyymqOSzoGJIkSZIkSUpD+XmjKS3el611zwQdpdduzMypc0mpGh5bxK1550LujY2wfh1UJ9kHtH4dNHVYBD4vDyr70H1SXBLbutPSDGtWxcuRCEyY1PvzpJOaKuehSZkoFApRNP1Utjz646Cj9MmSrwSdoP9EwjB+GLzaoe+tuZdduE2tieW85O9VOWQVzfD7TJIkSZIGs5qKI8nOKqaltS7oKL121a17t/+IUbC5wxySNSth/JTk9+88h2T4AM4hqaqJbd1ZtSKxPK4Xr6tXWTrdUqMt84bbJwiHshlddWLQMSRJSkuhUJiaqrezfNVvg47SJ9ffFXSC/tN5XEhTI2xYF6tPxoZO40Jy+zguBOD+exLL8w/a81iRTDbSMSGSJEmSJEmSFKjTTz+dBQsWcP/99xMKhWhoaOCSSy7hK1/5CpWVlbS1tbFp0+43wszLy+PLX/5y0udpbGzkuuuuIxQKAXD44Yen7DX0l3DQASRJkiRJkjR0jB0+dAdUjpmQWF66JPl9azfDxg4T2yJZMHJManKlwvNPJZZnz++n8zw5MOcZCNmRImoqjg46hiRJaatsxMnYlTn4ZWX13EZQPnLo/jtKkiRJkpTZqssPITe7IugYgbBvMKa1JbEcifTuXFmd2re3927/dDKU+8qVKCtvGIUj03+ykdLTYy9Ba4cb+c6fArkZvIhSyfiTCUUy+AVIkiRJkiSpX9V4Y/+0EwrBuE79YC8vTn7/lxYllsdNjB0z1Z59Glo7LEg/aw7k5KT+PAMlL3cUpcX7Bh1DUh8VTff7LF3NHJFY3rSjd/tvqk8sl+XvXZ6hoGj6qUFHkCRJkiT1o0g4hzHVJwUdIxBjJiaWl72U/L5bN8OmTnNIatJoDsmLneaQzNqvf86zdUtiuSjDF7YeWXE0udmlQceQJCltOSYkPaV8XMiEvo0LaWiAx/+TWLfg+N4fJ1PUVL096AiSJEmSJEmSNOT94Q9/YOTIkQCEQiGi0ShtbW2sW7eOjRs3Eo1GiUajhHZe+A6FQlx55ZVv7ZOMG264gW3bthGNRgFYsGBB6l9IirmcjCRJkiRJkgbM2OGn8tiLnwg6RiDmHgB33hAvL3kOotHkBuIueiaxPH0fyEuTm+CsWw0rl8bLNWNgysz+OdcTnQYfH3FC/5xnIIyqfhuRSG7QMSRJSlvZuRUUVx5O3caHgo6iFFq7MbE8rJt5+medAIf14YYHl/0UNm+Nlz/5PzB5XLw8orL3xwxaKJRN6YgTg44hSZIkSVKfhEMRxg5/O6+++aegoww4+wZjCktiN+LcpXYzjBid/Pm2bEosF2fwfR/HjvBmXIornXAaO1Y9GHQM7TSmCn758d7v988n4PYn4+VDpsP7jo2Xs8J7n62zuxYmls88JPXnGEglE/xslCRJkiRJUvdqqk7jpWWXBR1Dnex/ELzaYdG25xbCEUneY+u5Ttc49z8odbk6eviBxPJxGb7WXk3VaW/dFE1S5ikYfwThvFLaG7f23FgD6pgpcMsL8fKitb3bf/GaxPKEYbu3+eSR8J75vc/24b8llr97GlQUdjhXRe+PGbTc4bPJGTax54aSJEmSpIw2fvhpLFt9XdAxBtw+B8BdfZxDsrjTHJJpaTyHZMQYmNxP95db2mlR7fIMvD9IR+OcQyJJ0h5VVxxLJJxPW3tD0FHUyfxO40KeXwiHJzku5PnO40IO7luG/z4MTY3xctkwmHdA346V7oaVHkRe7oigY0iSJEmSJEnSkDd27FgeeughTj31VJYsWdLtfL5oNArA17/+dT7xieTXYYxGo1xxxRWEQqG3jnH66afvffB+lhV0AEmSJEmSJA0dRfljGVYyj83bng06yoCbNAOKSmD7tlh5w1p4+QWYPqfnfR++J7E8r59u7NYXN/05sXzECf1znhefhqVL4uVR42HitP4510AYO9yJaZIk9aR85GnUbXwo6BhKoYefSixPHtt1u7EjY1tv5eQklqdPhP1m9f446aSkegFZ2SVBx5AkSZIkqc/GDj+NV9/8U9AxBpx9gzGV1bB1c7z80vPJ/RnssuS5xHJVht6/p6RwCqWFGdy5qZQrnXAqqx/+fNAxtFNBLhzch7fos0sTy5UlfTtOsl5bDXd3uLlxWSEs2Kf/ztffQuFsSsaeGHQMSZIkSZIkpbHS4n3Jyx1FY9OqoKOogwMOgb916P579CH48Mcgv4eF2Brq4bFOQ8MPPDT1+VYsh0cejJdLSuGgfjjPQKqpch6alMlCkWwKp55E3fPXBh1FnRw+EXKzoKk1Vn55Pby+GcYP63nfVzfA0k2JdQeO273dtOq9zwmw32gYVZaaYwWlaLrfZ5IkSZI0FIypPolQKEI02hZ0lAE1cXriHJKNa+GVF2BaEvMnHuk0h2ROGs0hufWaxPLhx/fPedra4LF/J9ZNn9s/5xoo44afGnQESZLSWlakgOqK41mz4dago6iT/Q+GazuMC3nsP/DBZMeF/KfTsQ7pW4YH7k4sH7EAIpG+HSvd1VT5u1GSJEmSJEmS0sXEiRN57rnn+OUvf8nVV1/N008/nfB8Xl4exx57LJdccgmHHXZYr4597bXXsnjx4rfKc+fOZfz48amI3a/CQQeQJEmSJEnS0DJu+NC8OUk4DIcdm1h3698gGt3zfkueg1cXxct5+bD/4anP1xcLH4OnHo6Xq2pgwcmpP0/dVvjrrxLrzv1Q6s8zUEKEGVPdD39QkiQNMuUjh+bvxsHq9Tfh9gcS6444IJAoGcX3gSRJkiQp042qOoFwOCfoGAPOvsGYGZ1uNvngXdBYn9z51q2Gpx/d8/EyxdjhpxEKhYKOoTSSWzaF3PIZQcdQBmlshsuvhbb2eN0nT4Wc7OAy7a2iUUcTyS0NOoYkSZIkSZLSWCgUoqbKcZTpZvxEmDwtXm5sgJuv63m/m66DxsZ4eeoMGNPFIvF7o6kJfvn/oL3DtdT3fhCyM7i7MitSROWwBUHHkLSXiqf7fZaOCnLg1NmJdVc9kty+v3g4sXzAWKgoTE2uwapohu8DSZIkSRoK8nKGUTPsiKBjDLhwGA7pNIfkn9f2PIfkpefgtfi6Fmk1h+SZx+DpjnNIRsBR/XTbtLv+ARvWJNbNPah/zjUQhpXMobggxZ0gkiQNQiMdE5KWxk2ESVPj5cYGuCWJcSG3XA9NHcaFTOnjuJAN62DxC4l1R5/Q++NkCt8HkiRJkiRJkpResrKy+OQnP8mTTz7Jpk2beP7553nkkUdYtGgRW7Zs4bbbbuOwww7r9XFPOOEEli9f/tZ2zz339EP61AsHHUCSJEmSJElDy9jhQ3dg5dvOgtz8ePmVF+GOf3Tffssm+NNPEuuOOw2Kk1j74SOnJm4vvdDzPmtX9TxZbpeFj8Gvv5dYd95Fyd0MbusWqN+e3Hm2bIIffR3Wd5iYdtBRMGvf5PZPR8OHHU5eTkXQMSRJSnv5xVPIK54edIxB75BzE7eFi7pvu6MBvvdbWL+pd+dYuhI+8y1oaY3XjaiCYw7pW+ahpHzkqUFHkCRJkiRpr2RnFTGy4tieGw5C9g3CgUdCqMOMhbra2HFamve837Za+MW3oa3D9aSCIph7YHJ5081Q7iNX90on+P9Ff9v3U4nbU68GnShuewNs2Jpc22318Lnfwguvx+v2nQhnZvg19hLfA5IkSZIkSUpCjTf473fvOCFxe/G5nvd51/sSyzf9HRY93337Rc/DzZ0WwnjPB3o+T/0O2JzkuOXtdXDlpfDKknjdjNlw3EnJ7Z+uqitOJBLODTqGpL1UOPVtEM4KOsagN+PbidsTK3re5+OHQ26Hv5pbXoAbevgu/OvTcOeSxLoLDu193qEkUjScvFEHBB1DkiRJkjRAxg3R8fMndppD8uqLcNcN3bffsgn+/NPEumNOhaKSns/10dMSt5eTmEOyrhdzSJ55DH73/cS6dycxh+TBf8FLSfQzdHTvzXDbXxPr5hwI4yb37jjpZPwQfQ9IktRbNVVvB0JBxxj0zjkxcVuUxO+1d3YaF3LzdbB4D785uxoX8u739z4rwAP3JP5unTgFxo7v27HSXUHeeEqKZgcdQ5IkSZIkSQELRd36skkDoby8nNmzZ3PIIYcwY8YMcnP7Ps+voqKCcePGvbVVVGTG2nXOhpIkSZIkSdKAqijdj4LckdQ3rQ46yoArLoVTzoEbr47X3Xg1bN4Ab38nlO28ptjeDs/9F/72m9hzu5QNgxPO6L98N10Nb66AQxbAvINg5BgIR+LPt7fBK4vg/tvh6UcT9z3+dJi1b3Lnef3V2KJGBx4J8w+FyTMgryCxzfo18MRDcOcN0NgQr6+ugXdf0LfXly5c3EiSpOSVjzyNNS+/FHSMQDz3EjR1sQjka51uvtjcDE90c7PgqmEwYXTqMrW3w413w633wUFz4ZhDYM40GDUcQp3mELa0wpKlcOdDcNu/obUt/lwoBJ/7IOT1cGODoa6gbB65BWODjiFJkiRJ0l4bO+I03txwR9AxBpx9gzByLBx6DDxyb7zu+afgm5+DM94Ls/eH7Oz4c4318OTDcMtfoHZz4rFOOQcKCnv/OoOWmz2M4eWucqLdlU48jfULrww6RiCeXQaNLbvXv7IqsdzcAo+/3PUxqkpgUk3qsw2UtVvgPd+DY+bC8fvC/pOhtNNn3PqtcO+z8Lu7YXNdvL6sEC577+7X5TNN6YRTg44gSZIkSZKkDFA5bAFZkSJa27YHHWXALVkEzU2717++LLHc0gzPLez6GMMqYMy41Gfb7wCYNx+efTpWbm2Fb3wFzvswHH8S5ObF6hsb4J474C+/j7V5a/8DYU4SfU0b1sMXPwEHHwaHHgmz5kBxp0XfNm+Cxx6Cf/wNttbG60tK4ZNfyPxrqTVVzkOTBoNIfjkF44+kftm/g44SiIVvQGPr7vUvr08sN7fCo8u7PkZ1EUyuSn22ESXwkYPh5w/H6752OyxeCx85BGo6fO+s3gq/fhSueybxGKfMhMMnpj7bYFI0/VRC4XDQMSRJkiRJA2TciNN4bPHngo4x4IpK4KSz4eY/x+tu3jmH5ORzE+eQPP8EXNdpDknpMDjujP7Ld/OfYfUKOGgBzD0QarqYQ/LqInjgX/BMpzkkx54OM5O4rr/8FfjbVTB6Asw/DGbNh5qxifNGINb/8dJzcM9NsXN2VFgM53y4b68xXYwb4bV9SZKSkZc7nGGlB7F56+NBRwnES92MC1nRaVxIczM83824kPJ+Ghey7wEwdz48t3NcSFsrfPMr8N4PwXEdx4U0wn3/gr/8Idbmrf0PhH2SvD9xR9EoPHhvYt3Rx/ftNWSCkdWnEcr0gS2SJEmSJEmSpEEtK+gAkiRJkiRJGlpCoRBjh5/KSyt/FXSUQLztLHhtCTz/ZLzugTvgobugohryC2DjOqjfkbhfTg5ceDEUFPVvvnWr4OZrYltObjzTju1QuwmaGnff54gT4JwP9e48TY3wn7tjWygEFVWxyXttbbB1C2yr3X2fqhHw2ctj7TLZ2OEu4CFJUrLKR57Kmpe/G3SMQFz6U1i7oed2m7fCp7/Z9XMnHwVf+3hqcwG0tsEjC2MbQEE+VJRBUUGsvL0e1qyPtessFIJLLoQj9k99rsGmvMbfjZIkSZKkwWFs9dt5lIuCjhEI+wbhPRfCqtfh9dfidatWwM+/Ddk5sT7AvHyo3w7r18Ru5NnZ/MPghDN7/fLSwujqkwmHnbah3RUMP4is/CpaG5K4EDzIfOVqWLO553ab6uCin3f93KkHwuXnpTbXQGtpg7sWxjaA4WUwbOfn/sY62LgtdsO2jsoK4ecXwZh+WNhpIOVVzCGnpB/urCdJkiRJkqRBJxLOpbriBFavvzHoKAPu/30HNqzruV3tFrjskq6fW3A8fPKLqc21y6e+BF/+NKxbGys3N8PvfwnX/B6Gj4jVrVsTq+9oxEj45BeSP09rCzz8QGwDqKiE0rLY49otsGXz7tdSS0rha9+KnSuzhRleeXLQISSlSNH0U6lf9u+gYwTii7fC6q09t9u4Az78t66fO2MfuKKfplhcdDgsWgsP7BzXEAX++jT87WkYXQZl+VDbAG/U7r7vzBFwmR/VPSqa7vwYSZIkSRpKSgsnUVY0g9rtS4KOMuBOOAuWvgQvdJhD8tAd8PBdMGznfI1NXcwhyc6B8780MHNIbr0mtuXk7syUH8vT3RySw06Asz7Yu/O8uTy23XINhCMwrDL22iPZsbkjm9YnLo69S14+fPxrUFXTt9eXDgpyR1BVOj/oGJIkZYyaqlPZvPXxoGME4idXJjcuZOsW+MaXu37uqOPhE70Yg9Ebn/gifPUzsH7nuJCWZvjjVfDXP8TGhUSjsTEjLZ3GhQyvgY9/vm/nfOnF2FiTXbKy4fAFfTtWJqipsg9NkiRJkiRJkpTewkEHkCRJkiRJ0tAzYeS5QUcITDgMF10CBx6ZWN/eDhvWwsplu09MKyqGT10KU2YOWEwAmptgzRuw7OXYpLXOE9NycuBd58P7Pxl7XX0VjcLG9bEFj95YDttqd28z7yD4yvdjix9lsmHFcygtmhp0DEmSMkZxxSHk5I8KOoZ6UN8Ab6yBJUtj2xtroLVt93ZjauCnX4PTjhn4jJmoYsw5QUeQJEmSJCklCvNHU11+aNAxAmHfIOTmwWe/AfMP2/25lmZYvTJ2zrWrYn8uHYVCcPzpcP7nY48z0YQar/Goa6FwhNJJZwUdQ2lkXS0seTO2bdi6+2J98ybCX74AM8cGEi+lyqb42ShJkiRJkqTkjRo+dOehpbOycrjsezB+YmJ9cxO8sSK2NXda4GLCJLj8u1Ba1vfzbtoIy16LbZs37X4tdfos+O5PYdIgmL5VNWwBuTmVQceQlCLFs87K3I7vQS4Shh+/A87YJ7E+CrxRCy+sif23s2OmwNXnQWHOAITMYOHcEgonnxB0DEmSJEnSAJs0RO8xFw7DBRfD/kck1re3w8a18EYXc0gKi+ET/weTA5hDsvYNWP5K13NIsnPgnI/A/3xi7+4v194GG9fF7i33+iuwfjW0te7ebsJU+Or/g4nT+36udDCh5mxCIZf+kCQpWaOHO8coXZWVw6XfhXHdjAt5c2VsjnBH4yfF9unruJD770kszz8Iikv6dqx0l5tdRVX5UUHHkCRJkiRJkiRpj7KCDiBJkiRJkqShp6biaEoKJrOt/rWgowQiOwcu+CLMPxT+eV1sQlpXcvPg0GPg1HdDSVn/5zr0WGhpgZdfhKaG7tsVFsPBR8OJ74Bhfbh/2pgJcPRJ8MLTsGl99+3CYZgxD044HWbt1/vzpKNp4y4MOoIkSRklFIpQNeEjrFp8WdBRBBQVwC8uhceegacXwauvQ0sXNxXoKCsC82bC24+GBQdDTvYABB0EiioOoaB0n54bSpIkSZKUIaaPvYD1Wx4NOkYghnrfIEBhEVx0CSxaCPf9ExY90/XNKnfJyYX9DoHjToPxU/p2znRQkDeKMdUnBx1Daaxi1vlsevGqoGMoAMPL4LwF8PAieH0P4yZCIZg7Ad59JBy/7yBZDysUoWLGh4JOIUmSJEmSpAxSU30GOdmVNLdsDDqKOqkeDlf+FP55E9x+E2ze1HW7YRXw9jPhlDMhuxdjiSur4NSzYOF/YdWb3bcLhWDaTDjlDDj0yEFyLRWYMNp5aNJgkl02hsKpJ7Pj5duDjqIu5GTBFafCyTPhqkdgYTffOyFgn5Hw0cNgQQaPZxhIpfu9n3B2XtAxJEmSJEkDbNqYD7HwlW8QpT3oKAMuOwc+8kXY7zC44+/wxvKu2+XmwcHHwCnvGpg5JIccC60t8EoSc0gOPBpOOBPKezmH5KiToKAIXlsEq1bsed4IxP6sps6Go06BffYfHNf3Z4y7IOgIkiRllOLCKVSVL2DDlvuDjqIuVA2HK34C/7oJbr8ZtnQzLqS8IjYm5OQzejcupKOmRnj8P4l1C47v27EywfhRHyIc9oZ8kiRJkiRJkqT0FopGo9GgQ0iSJEmSJGnoeWHZD3li8eeDjpEW1q2G5a/EBvK2tUJBIdSMgckzYpOzBlp7G7y5AtaugtpN0NwEkUhsgtyocTB2IoQjqTnXlk2xyXmb1kFDPbS3x15/9UiYODU2kW2wyI4U8a7jVpGTXRJ0FEmSMkpzwyoW3j4Oom1BR1EnzS2w/E1YtQ42bYEdO29wUFQAxYUwbhRMGgvZWcHmzESTDvwzVePOCzqGJEmSJEkp09rWwLX3jqapZXPQUQI3lPsGd2lughVLYf1q2LE9Vs7Lj/UNjhwLYybEMmS6/aZezr5TvxZ0DKW5V64/hPq1jwcdQwHaXAevrIJVm2BrPbS1Q2EejBoG+4yHYcVBJ0yt0slnM+Gk64OOIUmSJEmSpAyz6NUv88rr3wk6hvagvR2WvgqvL4OttbG60jKYMAkmToZweO+Ov7UWli+F9Wthex20tUFBAVSPgCnToax8L19AmsnNGcHbjljpQhfSILP95X/x5tWnBB1DSVhXB8++Cau3QVMrlORBVRHsNxoqCoNOl1kmfHoxudUzgo4hSZIkSQrAXU+cwevrbgk6RuDW75xDUrtzDkn+zjkkkwKcQ7JqBaxbBbWbobkRIllQXAajxsKYFM0haW2BtW/CxnWx8zQ1xF5/XkFs7sjwkbFzZQ2iy+Ajhh3B6Yc9FHQMSZIyzptr/8Hjz58TdAz1oL0dlr0KKzqNCxk/CSakYFzI0BLipMOXUlgwIeggkiRJkiRJCsCiRYuYPXv2W+Xrvg6TRgYYKAMtXQ3nXh4vv/jii8yaNSu4QNIg5jIzkiRJkiRJCsTU0R/g6Ze+Slt7Y9BRAjd8ZGxLF+FIbBGjsRP7/1zlFbFtKJg0+jxyskuCjiFJUsbJyR/FsJFnsHnVDUFHUSc52TBtQmxT6mTlVFAx+uygY0iSJEmSlFJZkXymjvkQLyz7ftBRAjeU+wZ3ycmFKTNj22AVCmUxbexHgo6hDFC5z8dYufbxoGMoQMOK4eDpQacYOJWzLwo6giRJkiRJkjLQ+NEX8srrVwLRoKOoG+EwTJkW2/pDaRnMm98/x05HE0ZfQDg8iFZ8kwRA4ZQTyS6fQMuW5UFHUQ+GF8OJM4JOkfkKJhxNbrV/kJIkSZI0VM0cfxGvr7sl6BiBqx4Z29JFOAJjJsa2/pSVDaMnxLahYtb4jwUdQZKkjDSy+nTycmtobFoTdBTtQTgMk6fFNu2dEZUnU1gwhH4oS5IkSZIkac/ad25Knn9e0oAJBx1AkiRJkiRJQ1NuzjAmjnp30DGkATNjnAt4SJLUV8MnO8FbQ0fVhA8TjuQFHUOSJEmSpJSbPu6jQUeQBsz4EWdSkFcTdAxlgLLJ5xDJqwg6hjQgcsumUTR6QdAxJEmSJEmSlIEK88czovKUoGNIAyIUijB+1PlBx5DUD0LhCGUHOnZCQ0fZQc4HkyRJkqShbHTV8ZQUTAo6hjQg8nOqmVDzjqBjSJKUkcLhbCbYR64hZNIY+9AkSZIkSZIkSZkhHHQASZIkSZIkDV0zxjngUkPD8GGHM6xkTtAxJEnKWCVVC8grnhZ0DGkAhBg+8cKgQ0iSJEmS1C9KCicxuuptQceQBoR94UpWOCuPipkfDjqGNCAq53yMUCgUdAxJkiRJkiRlqAne+F9DRE3V6eTnjQ46hqR+Ujr/Q4SycoOOIfW7SNEIimeeEXQMSZIkSVKAQqEwM8dfFHQMaUBMH3c+kXBO0DEkScpYE0afTygUCTqG1O8K8ycwovLEoGNIkiRJkiRJkpSUcNABJEmSJEmSNHRVle1PZekBQceQ+p2LG0mStHdCoRDDJ/l9qsGvbMRJ5BVNDDqGJEmSJEn9ZsZ4r/Fo8CsrmsGIiqOCjqEMUjH7QiAUdAypX4WzCiif/r6gY0iSJEmSJCmDDa84kYL8CUHHkPrdhNH2qUqDWVZhJcWzzw06htTvyg64gFAkO+gYkiRJkqSATRvzASLhvKBjSP0qRJgZYy8IOoYkSRmtIG80I6tODzqG1O8mjv4ooVAk6BiSJEmSJEmSJCUlHHQASZIkSZIkDW0ucKTBLi+nmvEj3hF0DEmSMl7VuPcRjhQEHUPqV8Mn++8jSZIkSdLgNrr6ZArzxwYdQ+pXM8Z9jFAoFHQMZZDc0omUjDsp6BhSvyqf9l6ycsuCjiFJkiRJkqQMFgqFmTD6oqBjSP2qqGAaVcOOCTqGpH5WfpDzBjTIhSOUHXB+0CkkSZIkSWkgL6eCSaPeFXQMqV+NG34qxQXOlZIkaW9NHGMfmga3cDiX8aM+FHQMSZIkSZIkSZKSFg46gCRJkiRJkoa2iSPfSU52edAxpH4zbexHiERyg44hSVLGy8opo3Lse4OOIfWb3ILxlI14W9AxJEmSJEnqV+FQhBnjPhp0DKnfZEUKmTz6f4KOoQxUsY83Z9Pg5v/jkiRJkiRJSoVxIz9IOOwcHQ1eE8ZcRCgUCjqGpH6WN+Ygcmv2DTqG1G+KZpxOdunooGNIkiRJktLErPGOIdXgNtP/xyVJSonqYcdQXDAt6BhSvxk9/FxycyqDjiFJkiRJkiRJUtLCQQeQJEmSJEnS0JYVyXeBIw1a4VA2M8ZdFHQMSZIGjRFTPhV0BKnfjJjySUKhSNAxJEmSJEnqd9PGnk9WpCDoGFK/mDrmg+RklwYdQxmoZNzbyC2bGnQMqV8UjjySgqp5QceQJEmSJEnSIJCbU8mYEecFHUPqF1lZJYyr+UDQMSQNgFAoxLDDPhN0DKnfDDvk00FHkCRJkiSlkeqyAxhefmjQMaR+UVY0g9FVxwUdQ5KkQSEUCjF5nH0MGrwmj/UeipIkSZIkSdpdKOrWl03SwAgHHUCSJEmSJEmaPfHzZGeVBB1DSrnp4y6kMH900DEkSRo0CkpnUzHmnUHHkFIuO28k1RM/GnQMSZIkSZIGRF5OJTPHe4MWDT6RcD5zJn856BjKUKFwhBEHXRp0DKlf1Bz8jaAjSJIkSZIkaRCZNvGrhELZQceQUm7KuC+QnV0adAxJA6RkznvIqZwWdAwp5QomHUfBhCODjiFJkiRJSjP7T7s86AhSvzhg2uWEQi71IUlSqowf9SEK8sYFHUNKuZFVpzOsdP+gY0iSJEmSJEmS1CuOiJAkSZIkSVLg8nIqmDPpS0HHkFIqK1LIvCn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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "# Plot graph\n",
-        "plot_graph(graph, length, beams, 'sequence')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qDd_PePe_Itd"
-      },
-      "source": [
-        "## 🔬 Nucleus sampling"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "exsgQ3NQ8oTS"
-      },
-      "outputs": [],
-      "source": [
-        "def nucleus_sampling(logits, temperature, p, beams, plot=True):\n",
-        "    assert p > 0\n",
-        "    assert p <= 1\n",
-        "\n",
-        "    # Sort the probabilities in descending order and compute cumulative probabilities\n",
-        "    sorted_logits, sorted_indices = torch.sort(logits, descending=True)\n",
-        "    probabilities = torch.nn.functional.softmax(sorted_logits / temperature, dim=-1)\n",
-        "    cumulative_probabilities = torch.cumsum(probabilities, dim=-1)\n",
-        "\n",
-        "    # Create a mask for probabilities that are in the top-p\n",
-        "    mask = cumulative_probabilities < p\n",
-        "\n",
-        "    # If there's not n index where cumulative_probabilities < p, we use the top n tokens instead\n",
-        "    if mask.sum() > beams:\n",
-        "        top_p_index_to_keep = torch.where(mask)[0][-1].detach().cpu().tolist()\n",
-        "    else:\n",
-        "        top_p_index_to_keep = beams\n",
-        "\n",
-        "    # Only keep top-p indices\n",
-        "    indices_to_remove = sorted_indices[top_p_index_to_keep:]\n",
-        "    sorted_logits[indices_to_remove] = float('-inf')\n",
-        "\n",
-        "    # Sample n tokens from the resulting distribution\n",
-        "    probabilities = torch.nn.functional.softmax(sorted_logits / temperature, dim=-1)\n",
-        "    next_tokens = torch.multinomial(probabilities, beams)\n",
-        "\n",
-        "    # Plot distribution\n",
-        "    if plot:\n",
-        "        total_prob = torch.nn.functional.softmax(logits / temperature, dim=-1)\n",
-        "        plot_prob_distribution(total_prob, next_tokens, 'nucleus', top_p_index_to_keep)\n",
-        "\n",
-        "    return next_tokens\n",
-        "\n",
-        "# Start generating text\n",
-        "beam_search(input_ids, 0, bar, length, beams, 'nucleus', 1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "p6uTqcMnk0qb",
-        "outputId": "7bf6678b-e1a9-43aa-b935-6a92eccc2dbf"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Generated text: I have a dream. I have a dream\n"
-          ]
-        }
-      ],
-      "source": [
-        "sequence, max_score = get_best_sequence(graph)\n",
-        "print(f\"Generated text: {sequence}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 363
-        },
-        "id": "NPHx7ZRL2641",
-        "outputId": "48b197b7-34ea-44c6-f7e6-46791083ccb5"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 12420x2100 with 2 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "# Plot graph\n",
-        "plot_graph(graph, length, beams, 'sequence')"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4",
-      "authorship_tag": "ABX9TyNHyR+CsHtB4LQo1w5rfXe4",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "widgets": {
-      "application/vnd.jupyter.widget-state+json": {
-        "9713d401d53341e38141920ea03dbd5a": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "HBoxModel",
-          "model_module_version": "1.5.0",
-          "state": {
-            "_dom_classes": [],
-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "HBoxModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HBoxView",
-            "box_style": "",
-            "children": [
-              "IPY_MODEL_7a4520eef6b046049ac015596f45e765",
-              "IPY_MODEL_f630193167224e92aef625d875d74f03",
-              "IPY_MODEL_8367416a0fae4c46887b947b4627c9ed"
-            ],
-            "layout": "IPY_MODEL_3d8a0c7938274453989b93a819dfbaae"
-          }
-        },
-        "7a4520eef6b046049ac015596f45e765": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "HTMLModel",
-          "model_module_version": "1.5.0",
-          "state": {
-            "_dom_classes": [],
-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "HTMLModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HTMLView",
-            "description": "",
-            "description_tooltip": null,
-            "layout": "IPY_MODEL_24dfcd472872492ab9dd78704d01f391",
-            "placeholder": "​",
-            "style": "IPY_MODEL_928ac9d1df6348da8b937dd4baed73bc",
-            "value": "Downloading (…)lve/main/config.json: 100%"
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diff --git a/Fine_tune_LLMs_with_Axolotl.ipynb b/Fine_tune_LLMs_with_Axolotl.ipynb
deleted file mode 100644
index edc6e0c..0000000
--- a/Fine_tune_LLMs_with_Axolotl.ipynb
+++ /dev/null
@@ -1,1555 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Fine_tune_LLMs_with_Axolotl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Fine-tune LLMs with Axolotl\n",
-        "\n",
-        "> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne), based on [Giorgio](https://github.com/g-i-o-r-g-i-o)'s notebook and Axolotl's [example](https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/examples/colab-notebooks/colab-axolotl-example.ipynb)."
-      ],
-      "metadata": {
-        "id": "mL-BPZyZ0gtV"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!git clone -q https://github.com/OpenAccess-AI-Collective/axolotl\n",
-        "%cd axolotl\n",
-        "!pip install -qqq packaging huggingface_hub --progress-bar off\n",
-        "!pip install -qqq -e '.[flash-attn,deepspeed]' --progress-bar off"
-      ],
-      "metadata": {
-        "id": "BI6B0Bfe0hxr"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import yaml\n",
-        "\n",
-        "new_model = \"mlabonne/TinyAlpaca\"\n",
-        "yaml_string = \"\"\"\n",
-        "base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\n",
-        "model_type: LlamaForCausalLM\n",
-        "tokenizer_type: LlamaTokenizer\n",
-        "is_llama_derived_model: true\n",
-        "\n",
-        "load_in_8bit: false\n",
-        "load_in_4bit: true\n",
-        "strict: false\n",
-        "\n",
-        "datasets:\n",
-        "  - path: mhenrichsen/alpaca_2k_test\n",
-        "    type: alpaca\n",
-        "dataset_prepared_path:\n",
-        "val_set_size: 0.05\n",
-        "output_dir: ./qlora-out\n",
-        "\n",
-        "adapter: qlora\n",
-        "lora_model_dir:\n",
-        "\n",
-        "sequence_len: 1096\n",
-        "sample_packing: true\n",
-        "pad_to_sequence_len: true\n",
-        "\n",
-        "lora_r: 32\n",
-        "lora_alpha: 16\n",
-        "lora_dropout: 0.05\n",
-        "lora_target_modules:\n",
-        "lora_target_linear: true\n",
-        "lora_fan_in_fan_out:\n",
-        "\n",
-        "wandb_project:\n",
-        "wandb_entity:\n",
-        "wandb_watch:\n",
-        "wandb_name:\n",
-        "wandb_log_model:\n",
-        "\n",
-        "mlflow_experiment_name: colab-example\n",
-        "\n",
-        "gradient_accumulation_steps: 1\n",
-        "micro_batch_size: 1\n",
-        "num_epochs: 4\n",
-        "max_steps: 20\n",
-        "optimizer: paged_adamw_32bit\n",
-        "lr_scheduler: cosine\n",
-        "learning_rate: 0.0002\n",
-        "\n",
-        "train_on_inputs: false\n",
-        "group_by_length: false\n",
-        "bf16: false\n",
-        "fp16: true\n",
-        "tf32: false\n",
-        "\n",
-        "gradient_checkpointing: true\n",
-        "early_stopping_patience:\n",
-        "resume_from_checkpoint:\n",
-        "local_rank:\n",
-        "logging_steps: 1\n",
-        "xformers_attention:\n",
-        "flash_attention: false\n",
-        "\n",
-        "warmup_steps: 10\n",
-        "evals_per_epoch:\n",
-        "saves_per_epoch:\n",
-        "debug:\n",
-        "deepspeed:\n",
-        "weight_decay: 0.0\n",
-        "fsdp:\n",
-        "fsdp_config:\n",
-        "special_tokens:\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "# Convert the YAML string to a Python dictionary\n",
-        "yaml_dict = yaml.safe_load(yaml_string)\n",
-        "\n",
-        "# Specify your file path\n",
-        "yaml_file = 'config.yaml'\n",
-        "\n",
-        "# Write the YAML file\n",
-        "with open(yaml_file, 'w') as file:\n",
-        "    yaml.dump(yaml_dict, file)"
-      ],
-      "metadata": {
-        "id": "70zJf1hi0huQ"
-      },
-      "execution_count": 5,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ffcc0IB9Sr-z",
-        "outputId": "f64291b0-0867-440b-b7b9-683fa66e3a08"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "The following values were not passed to `accelerate launch` and had defaults used instead:\n",
-            "\t`--num_processes` was set to a value of `1`\n",
-            "\t`--num_machines` was set to a value of `1`\n",
-            "\t`--mixed_precision` was set to a value of `'no'`\n",
-            "\t`--dynamo_backend` was set to a value of `'no'`\n",
-            "To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\n",
-            "2024-01-27 22:05:58.426793: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-            "2024-01-27 22:05:58.426851: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-            "2024-01-27 22:05:58.428152: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-            "2024-01-27 22:05:59.454203: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
-            "[2024-01-27 22:06:00,824] [INFO] [datasets.<module>:58] [PID:2797] PyTorch version 2.1.2 available.\n",
-            "[2024-01-27 22:06:00,825] [INFO] [datasets.<module>:95] [PID:2797] TensorFlow version 2.15.0 available.\n",
-            "[2024-01-27 22:06:00,827] [INFO] [datasets.<module>:108] [PID:2797] JAX version 0.4.23 available.\n",
-            "[2024-01-27 22:06:01,844] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
-            "config.json: 100% 560/560 [00:00<00:00, 3.84MB/s]\n",
-            "[2024-01-27 22:06:03,638] [INFO] [axolotl.normalize_config:170] [PID:2797] [RANK:0] GPU memory usage baseline: 0.000GB (+0.255GB misc)\u001b[39m\n",
-            "                                 dP            dP   dP \n",
-            "                                 88            88   88 \n",
-            "      .d8888b. dP.  .dP .d8888b. 88 .d8888b. d8888P 88 \n",
-            "      88'  `88  `8bd8'  88'  `88 88 88'  `88   88   88 \n",
-            "      88.  .88  .d88b.  88.  .88 88 88.  .88   88   88 \n",
-            "      `88888P8 dP'  `dP `88888P' dP `88888P'   dP   dP \n",
-            "                                                       \n",
-            "                                                       \n",
-            "\n",
-            "\u001b[33m[2024-01-27 22:06:03,642] [WARNING] [axolotl.scripts.check_user_token:382] [PID:2797] [RANK:0] Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets.\u001b[39m\n",
-            "tokenizer_config.json: 100% 776/776 [00:00<00:00, 4.70MB/s]\n",
-            "tokenizer.model: 100% 500k/500k [00:00<00:00, 1.77MB/s]\n",
-            "special_tokens_map.json: 100% 414/414 [00:00<00:00, 1.73MB/s]\n",
-            "tokenizer.json: 100% 1.84M/1.84M [00:00<00:00, 4.52MB/s]\n",
-            "[2024-01-27 22:06:05,850] [DEBUG] [axolotl.load_tokenizer:216] [PID:2797] [RANK:0] EOS: 2 / </s>\u001b[39m\n",
-            "[2024-01-27 22:06:05,850] [DEBUG] [axolotl.load_tokenizer:217] [PID:2797] [RANK:0] BOS: 1 / <s>\u001b[39m\n",
-            "[2024-01-27 22:06:05,850] [DEBUG] [axolotl.load_tokenizer:218] [PID:2797] [RANK:0] PAD: 2 / </s>\u001b[39m\n",
-            "[2024-01-27 22:06:05,850] [DEBUG] [axolotl.load_tokenizer:219] [PID:2797] [RANK:0] UNK: 0 / <unk>\u001b[39m\n",
-            "[2024-01-27 22:06:05,850] [INFO] [axolotl.load_tokenizer:230] [PID:2797] [RANK:0] No Chat template selected. Consider adding a chat template for easier inference.\u001b[39m\n",
-            "[2024-01-27 22:06:05,850] [INFO] [axolotl.load_tokenized_prepared_datasets:182] [PID:2797] [RANK:0] Unable to find prepared dataset in last_run_prepared/c0112363192ff19da1e486577d4bf28b\u001b[39m\n",
-            "[2024-01-27 22:06:05,850] [INFO] [axolotl.load_tokenized_prepared_datasets:183] [PID:2797] [RANK:0] Loading raw datasets...\u001b[39m\n",
-            "\u001b[33m[2024-01-27 22:06:05,850] [WARNING] [axolotl.load_tokenized_prepared_datasets:185] [PID:2797] [RANK:0] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset.\u001b[39m\n",
-            "[2024-01-27 22:06:05,850] [INFO] [axolotl.load_tokenized_prepared_datasets:192] [PID:2797] [RANK:0] No seed provided, using default seed of 42\u001b[39m\n",
-            "Downloading readme: 100% 28.0/28.0 [00:00<00:00, 219kB/s]\n",
-            "Downloading data: 100% 1.76M/1.76M [00:00<00:00, 2.45MB/s]\n",
-            "Generating train split: 2000 examples [00:00, 37778.35 examples/s]\n",
-            "Tokenizing Prompts (num_proc=2): 100% 2000/2000 [00:05<00:00, 352.09 examples/s]\n",
-            "[2024-01-27 22:06:17,586] [INFO] [axolotl.load_tokenized_prepared_datasets:395] [PID:2797] [RANK:0] merging datasets\u001b[39m\n",
-            "Dropping Long Sequences (num_proc=2): 100% 2000/2000 [00:00<00:00, 2927.43 examples/s]\n",
-            "Add position_id column (Sample Packing) (num_proc=2): 100% 2000/2000 [00:00<00:00, 3089.00 examples/s]\n",
-            "[2024-01-27 22:06:19,041] [INFO] [axolotl.load_tokenized_prepared_datasets:405] [PID:2797] [RANK:0] Saving merged prepared dataset to disk... last_run_prepared/c0112363192ff19da1e486577d4bf28b\u001b[39m\n",
-            "Saving the dataset (1/1 shards): 100% 2000/2000 [00:00<00:00, 142755.66 examples/s]\n",
-            "[2024-01-27 22:06:19,067] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] total_num_tokens: 22777\u001b[39m\n",
-            "[2024-01-27 22:06:19,069] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] `total_supervised_tokens: 16719`\u001b[39m\n",
-            "[2024-01-27 22:06:24,965] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 1.0 total_num_tokens per device: 22777\u001b[39m\n",
-            "[2024-01-27 22:06:24,965] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] data_loader_len: 19\u001b[39m\n",
-            "[2024-01-27 22:06:24,966] [INFO] [axolotl.log:61] [PID:2797] [RANK:0] sample_packing_eff_est across ranks: [0.8659139294403893]\u001b[39m\n",
-            "[2024-01-27 22:06:24,966] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] sample_packing_eff_est: None\u001b[39m\n",
-            "[2024-01-27 22:06:24,966] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] total_num_steps: 76\u001b[39m\n",
-            "[2024-01-27 22:06:24,970] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] total_num_tokens: 414041\u001b[39m\n",
-            "[2024-01-27 22:06:25,001] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] `total_supervised_tokens: 294246`\u001b[39m\n",
-            "[2024-01-27 22:06:25,009] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 1.0 total_num_tokens per device: 414041\u001b[39m\n",
-            "[2024-01-27 22:06:25,009] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] data_loader_len: 372\u001b[39m\n",
-            "[2024-01-27 22:06:25,009] [INFO] [axolotl.log:61] [PID:2797] [RANK:0] sample_packing_eff_est across ranks: [0.8624991667499917]\u001b[39m\n",
-            "[2024-01-27 22:06:25,009] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] sample_packing_eff_est: 0.87\u001b[39m\n",
-            "[2024-01-27 22:06:25,009] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] total_num_steps: 1488\u001b[39m\n",
-            "[2024-01-27 22:06:25,009] [INFO] [axolotl.prepare_dataset:115] [PID:2797] [RANK:0] Maximum number of steps set at 20\u001b[39m\n",
-            "[2024-01-27 22:06:25,010] [DEBUG] [axolotl.train.log:61] [PID:2797] [RANK:0] loading tokenizer... TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\u001b[39m\n",
-            "[2024-01-27 22:06:25,574] [DEBUG] [axolotl.load_tokenizer:216] [PID:2797] [RANK:0] EOS: 2 / </s>\u001b[39m\n",
-            "[2024-01-27 22:06:25,575] [DEBUG] [axolotl.load_tokenizer:217] [PID:2797] [RANK:0] BOS: 1 / <s>\u001b[39m\n",
-            "[2024-01-27 22:06:25,575] [DEBUG] [axolotl.load_tokenizer:218] [PID:2797] [RANK:0] PAD: 2 / </s>\u001b[39m\n",
-            "[2024-01-27 22:06:25,575] [DEBUG] [axolotl.load_tokenizer:219] [PID:2797] [RANK:0] UNK: 0 / <unk>\u001b[39m\n",
-            "[2024-01-27 22:06:25,575] [INFO] [axolotl.load_tokenizer:230] [PID:2797] [RANK:0] No Chat template selected. Consider adding a chat template for easier inference.\u001b[39m\n",
-            "[2024-01-27 22:06:25,575] [DEBUG] [axolotl.train.log:61] [PID:2797] [RANK:0] loading model and peft_config...\u001b[39m\n",
-            "[2024-01-27 22:06:25,678] [INFO] [axolotl.load_model:372] [PID:2797] [RANK:0] patching _expand_mask\u001b[39m\n",
-            "model.safetensors: 100% 4.40G/4.40G [01:42<00:00, 42.8MB/s]\n",
-            "generation_config.json: 100% 129/129 [00:00<00:00, 758kB/s]\n",
-            "[2024-01-27 22:08:13,799] [INFO] [axolotl.load_model:641] [PID:2797] [RANK:0] GPU memory usage after model load: 0.753GB (+0.022GB cache, +0.368GB misc)\u001b[39m\n",
-            "[2024-01-27 22:08:13,825] [INFO] [axolotl.load_model:673] [PID:2797] [RANK:0] converting PEFT model w/ prepare_model_for_kbit_training\u001b[39m\n",
-            "[2024-01-27 22:08:13,829] [INFO] [axolotl.load_model:685] [PID:2797] [RANK:0] converting modules to torch.float16 for flash attention\u001b[39m\n",
-            "[2024-01-27 22:08:13,832] [INFO] [axolotl.load_lora:797] [PID:2797] [RANK:0] found linear modules: ['v_proj', 'k_proj', 'gate_proj', 'up_proj', 'o_proj', 'q_proj', 'down_proj']\u001b[39m\n",
-            "trainable params: 25,231,360 || all params: 1,125,279,744 || trainable%: 2.2422299996542017\n",
-            "[2024-01-27 22:08:14,263] [INFO] [axolotl.load_model:722] [PID:2797] [RANK:0] GPU memory usage after adapters: 0.847GB (+0.514GB cache, +0.368GB misc)\u001b[39m\n",
-            "[2024-01-27 22:08:14,273] [INFO] [axolotl.train.log:61] [PID:2797] [RANK:0] Pre-saving adapter config to ./qlora-out\u001b[39m\n",
-            "[2024-01-27 22:08:14,280] [INFO] [axolotl.train.log:61] [PID:2797] [RANK:0] Starting trainer...\u001b[39m\n",
-            "[2024-01-27 22:08:14,686] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 414041\u001b[39m\n",
-            "[2024-01-27 22:08:14,688] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 414041\u001b[39m\n",
-            "[2024-01-27 22:08:14,850] [INFO] [axolotl.callbacks.on_train_begin:601] [PID:2797] [RANK:0] The Axolotl config has been saved to the MLflow artifacts.\u001b[39m\n",
-            "  0% 0/20 [00:00<?, ?it/s][2024-01-27 22:08:14,853] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 414041\u001b[39m\n",
-            "{'loss': 1.6463, 'learning_rate': 2e-05, 'epoch': 0.0}\n",
-            "  5% 1/20 [00:02<00:45,  2.41s/it][2024-01-27 22:08:18,019] [INFO] [axolotl.callbacks.on_step_end:125] [PID:2797] [RANK:0] GPU memory usage while training: 0.963GB (+0.805GB cache, +0.638GB misc)\u001b[39m\n",
-            "{'loss': 4.8805, 'learning_rate': 4e-05, 'epoch': 0.0}\n",
-            "{'loss': 4.4957, 'learning_rate': 6e-05, 'epoch': 0.01}\n",
-            "{'loss': 4.4627, 'learning_rate': 8e-05, 'epoch': 0.01}\n",
-            "{'loss': 4.8849, 'learning_rate': 0.0001, 'epoch': 0.01}\n",
-            "{'loss': 5.0871, 'learning_rate': 0.00012, 'epoch': 0.01}\n",
-            "{'loss': 3.0099, 'learning_rate': 0.00014, 'epoch': 0.02}\n",
-            "{'loss': 4.4522, 'learning_rate': 0.00016, 'epoch': 0.02}\n",
-            "{'loss': 2.2085, 'learning_rate': 0.00018, 'epoch': 0.02}\n",
-            "{'loss': 4.4377, 'learning_rate': 0.0002, 'epoch': 0.02}\n",
-            "{'loss': 3.7593, 'learning_rate': 0.00019510565162951537, 'epoch': 0.03}\n",
-            "{'loss': 3.5486, 'learning_rate': 0.00018090169943749476, 'epoch': 0.03}\n",
-            "{'loss': 3.7615, 'learning_rate': 0.00015877852522924732, 'epoch': 0.03}\n",
-            "{'loss': 3.5778, 'learning_rate': 0.00013090169943749476, 'epoch': 0.03}\n",
-            "{'loss': 3.3562, 'learning_rate': 0.0001, 'epoch': 0.04}\n",
-            "{'loss': 3.7881, 'learning_rate': 6.909830056250527e-05, 'epoch': 0.04}\n",
-            "{'loss': 3.4147, 'learning_rate': 4.12214747707527e-05, 'epoch': 0.04}\n",
-            "{'loss': 3.7467, 'learning_rate': 1.9098300562505266e-05, 'epoch': 0.04}\n",
-            "{'loss': 3.6867, 'learning_rate': 4.8943483704846475e-06, 'epoch': 0.04}\n",
-            "{'loss': 4.0308, 'learning_rate': 0.0, 'epoch': 0.05}\n",
-            "100% 20/20 [00:17<00:00,  1.26it/s][2024-01-27 22:08:32,098] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "[2024-01-27 22:08:32,232] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "[2024-01-27 22:08:32,232] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            "  0% 0/22 [00:00<?, ?it/s]\u001b[A[2024-01-27 22:08:32,451] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            "  9% 2/22 [00:00<00:02,  9.16it/s]\u001b[A[2024-01-27 22:08:32,669] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 14% 3/22 [00:00<00:02,  6.46it/s]\u001b[A[2024-01-27 22:08:32,890] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 18% 4/22 [00:00<00:03,  5.58it/s]\u001b[A[2024-01-27 22:08:33,119] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 23% 5/22 [00:00<00:03,  5.08it/s]\u001b[A[2024-01-27 22:08:33,339] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 27% 6/22 [00:01<00:03,  4.89it/s]\u001b[A[2024-01-27 22:08:33,559] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 32% 7/22 [00:01<00:03,  4.78it/s]\u001b[A[2024-01-27 22:08:33,779] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 36% 8/22 [00:01<00:02,  4.70it/s]\u001b[A[2024-01-27 22:08:34,004] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 41% 9/22 [00:01<00:02,  4.62it/s]\u001b[A[2024-01-27 22:08:34,224] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 45% 10/22 [00:01<00:02,  4.59it/s]\u001b[A[2024-01-27 22:08:34,447] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 50% 11/22 [00:02<00:02,  4.56it/s]\u001b[A[2024-01-27 22:08:34,670] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 55% 12/22 [00:02<00:02,  4.54it/s]\u001b[A[2024-01-27 22:08:34,889] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 59% 13/22 [00:02<00:01,  4.55it/s]\u001b[A[2024-01-27 22:08:35,115] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 64% 14/22 [00:02<00:01,  4.51it/s]\u001b[A[2024-01-27 22:08:35,337] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 68% 15/22 [00:03<00:01,  4.51it/s]\u001b[A[2024-01-27 22:08:35,559] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 73% 16/22 [00:03<00:01,  4.51it/s]\u001b[A[2024-01-27 22:08:35,783] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 77% 17/22 [00:03<00:01,  4.49it/s]\u001b[A[2024-01-27 22:08:36,005] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 82% 18/22 [00:03<00:00,  4.50it/s]\u001b[A[2024-01-27 22:08:36,228] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 86% 19/22 [00:03<00:00,  4.50it/s]\u001b[A[2024-01-27 22:08:36,449] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 91% 20/22 [00:04<00:00,  4.50it/s]\u001b[A[2024-01-27 22:08:36,676] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            " 95% 21/22 [00:04<00:00,  4.47it/s]\u001b[A[2024-01-27 22:08:36,896] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            "100% 22/22 [00:04<00:00,  4.50it/s]\u001b[A[2024-01-27 22:08:37,116] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            "23it [00:04,  4.51it/s]            \u001b[A[2024-01-27 22:08:37,336] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            "24it [00:05,  4.52it/s]\u001b[A[2024-01-27 22:08:37,556] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
-            "\n",
-            "                                   \n",
-            "\u001b[A{'eval_loss': 3.513343572616577, 'eval_runtime': 5.5482, 'eval_samples_per_second': 18.024, 'eval_steps_per_second': 18.024, 'epoch': 0.05}\n",
-            "100% 20/20 [00:22<00:00,  1.26it/s]\n",
-            "25it [00:05,  4.53it/s]\u001b[A\n",
-            "{'train_runtime': 24.8459, 'train_samples_per_second': 0.805, 'train_steps_per_second': 0.805, 'train_loss': 3.8117882788181303, 'epoch': 0.05}\n",
-            "100% 20/20 [00:24<00:00,  1.24s/it]\n",
-            "[2024-01-27 22:08:39,580] [INFO] [axolotl.train.log:61] [PID:2797] [RANK:0] Training Completed!!! Saving pre-trained model to ./qlora-out\u001b[39m\n",
-            "(PeftModelForCausalLM(   (base_model): LoraModel(     (model): LlamaForCausalLM(       (model): LlamaModel(         (embed_tokens): Embedding(32000, 2048)         (layers): ModuleList(           (0-21): 22 x LlamaDecoderLayer(             (self_attn): LlamaSdpaAttention(               (q_proj): lora.Linear4bit(                 (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)                 (lora_dropout): ModuleDict(                   (default): Dropout(p=0.05, inplace=False)                 )                 (lora_A): ModuleDict(                   (default): Linear(in_features=2048, out_features=32, bias=False)                 )                 (lora_B): ModuleDict(                   (default): Linear(in_features=32, out_features=2048, bias=False)                 )                 (lora_embedding_A): ParameterDict()                 (lora_embedding_B): ParameterDict()               )               (k_proj): lora.Linear4bit(                 (base_layer): Linear4bit(in_features=2048, out_features=256, bias=False)                 (lora_dropout): ModuleDict(                   (default): Dropout(p=0.05, inplace=False)                 )                 (lora_A): ModuleDict(                   (default): Linear(in_features=2048, out_features=32, bias=False)                 )                 (lora_B): ModuleDict(                   (default): Linear(in_features=32, out_features=256, bias=False)                 )                 (lora_embedding_A): ParameterDict()                 (lora_embedding_B): ParameterDict()               )               (v_proj): lora.Linear4bit(                 (base_layer): Linear4bit(in_features=2048, out_features=256, bias=False)                 (lora_dropout): ModuleDict(                   (default): Dropout(p=0.05, inplace=False)                 )                 (lora_A): ModuleDict(                   (default): Linear(in_features=2048, out_features=32, bias=False)                 )                 (lora_B): ModuleDict(                   (default): Linear(in_features=32, out_features=256, bias=False)                 )                 (lora_embedding_A): ParameterDict()                 (lora_embedding_B): ParameterDict()               )               (o_proj): lora.Linear4bit(                 (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)                 (lora_dropout): ModuleDict(                   (default): Dropout(p=0.05, inplace=False)                 )                 (lora_A): ModuleDict(                   (default): Linear(in_features=2048, out_features=32, bias=False)                 )                 (lora_B): ModuleDict(                   (default): Linear(in_features=32, out_features=2048, bias=False)                 )                 (lora_embedding_A): ParameterDict()                 (lora_embedding_B): ParameterDict()               )               (rotary_emb): LlamaRotaryEmbedding()             )             (mlp): LlamaMLP(               (gate_proj): lora.Linear4bit(                 (base_layer): Linear4bit(in_features=2048, out_features=5632, bias=False)                 (lora_dropout): ModuleDict(                   (default): Dropout(p=0.05, inplace=False)                 )                 (lora_A): ModuleDict(                   (default): Linear(in_features=2048, out_features=32, bias=False)                 )                 (lora_B): ModuleDict(                   (default): Linear(in_features=32, out_features=5632, bias=False)                 )                 (lora_embedding_A): ParameterDict()                 (lora_embedding_B): ParameterDict()               )               (up_proj): lora.Linear4bit(                 (base_layer): Linear4bit(in_features=2048, out_features=5632, bias=False)                 (lora_dropout): ModuleDict(                   (default): Dropout(p=0.05, inplace=False)                 )                 (lora_A): ModuleDict(                   (default): Linear(in_features=2048, out_features=32, bias=False)                 )                 (lora_B): ModuleDict(                   (default): Linear(in_features=32, out_features=5632, bias=False)                 )                 (lora_embedding_A): ParameterDict()                 (lora_embedding_B): ParameterDict()               )               (down_proj): lora.Linear4bit(                 (base_layer): Linear4bit(in_features=5632, out_features=2048, bias=False)                 (lora_dropout): ModuleDict(                   (default): Dropout(p=0.05, inplace=False)                 )                 (lora_A): ModuleDict(                   (default): Linear(in_features=5632, out_features=32, bias=False)                 )                 (lora_B): ModuleDict(                   (default): Linear(in_features=32, out_features=2048, bias=False)                 )                 (lora_embedding_A): ParameterDict()                 (lora_embedding_B): ParameterDict()               )               (act_fn): SiLU()             )             (input_layernorm): LlamaRMSNorm()             (post_attention_layernorm): LlamaRMSNorm()           )         )         (norm): LlamaRMSNorm()       )       (lm_head): Linear(in_features=2048, out_features=32000, bias=False)     )   ) ), LlamaTokenizer(name_or_path='TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T', vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '</s>'}, clean_up_tokenization_spaces=False),  added_tokens_decoder={ \t0: AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), \t1: AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), \t2: AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), })\n",
-            "\u001b[0m"
-          ]
-        }
-      ],
-      "source": [
-        "!accelerate launch -m axolotl.cli.train config.yaml"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!python3 -m axolotl.cli.merge_lora config.yaml --lora_model_dir=\"./qlora-out\""
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5GENnc9Z8v-P",
-        "outputId": "762e6290-9877-485c-c084-5370c3b3d7f3"
-      },
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "mkdir: cannot create directory ‘final_model’: File exists\n",
-            "2024-01-27 22:10:36.876247: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-            "2024-01-27 22:10:36.876317: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-            "2024-01-27 22:10:36.878122: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-            "2024-01-27 22:10:38.390922: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
-            "[2024-01-27 22:10:39,447] [INFO] [datasets.<module>:58] [PID:4079] PyTorch version 2.1.2 available.\n",
-            "[2024-01-27 22:10:39,448] [INFO] [datasets.<module>:95] [PID:4079] TensorFlow version 2.15.0 available.\n",
-            "[2024-01-27 22:10:39,449] [INFO] [datasets.<module>:108] [PID:4079] JAX version 0.4.23 available.\n",
-            "[2024-01-27 22:10:40,350] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
-            "                                 dP            dP   dP \n",
-            "                                 88            88   88 \n",
-            "      .d8888b. dP.  .dP .d8888b. 88 .d8888b. d8888P 88 \n",
-            "      88'  `88  `8bd8'  88'  `88 88 88'  `88   88   88 \n",
-            "      88.  .88  .d88b.  88.  .88 88 88.  .88   88   88 \n",
-            "      `88888P8 dP'  `dP `88888P' dP `88888P'   dP   dP \n",
-            "                                                       \n",
-            "                                                       \n",
-            "\n",
-            "[2024-01-27 22:10:41,928] [INFO] [axolotl.normalize_config:170] [PID:4079] [RANK:0] GPU memory usage baseline: 0.000GB (+0.255GB misc)\u001b[39m\n",
-            "[2024-01-27 22:10:41,928] [INFO] [axolotl.common.cli.load_model_and_tokenizer:49] [PID:4079] [RANK:0] loading tokenizer... TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\u001b[39m\n",
-            "[2024-01-27 22:10:42,234] [DEBUG] [axolotl.load_tokenizer:216] [PID:4079] [RANK:0] EOS: 2 / </s>\u001b[39m\n",
-            "[2024-01-27 22:10:42,234] [DEBUG] [axolotl.load_tokenizer:217] [PID:4079] [RANK:0] BOS: 1 / <s>\u001b[39m\n",
-            "[2024-01-27 22:10:42,234] [DEBUG] [axolotl.load_tokenizer:218] [PID:4079] [RANK:0] PAD: 2 / </s>\u001b[39m\n",
-            "[2024-01-27 22:10:42,234] [DEBUG] [axolotl.load_tokenizer:219] [PID:4079] [RANK:0] UNK: 0 / <unk>\u001b[39m\n",
-            "[2024-01-27 22:10:42,235] [INFO] [axolotl.load_tokenizer:230] [PID:4079] [RANK:0] No Chat template selected. Consider adding a chat template for easier inference.\u001b[39m\n",
-            "[2024-01-27 22:10:42,235] [INFO] [axolotl.common.cli.load_model_and_tokenizer:51] [PID:4079] [RANK:0] loading model and (optionally) peft_config...\u001b[39m\n",
-            "[2024-01-27 22:10:42,320] [INFO] [axolotl.load_model:372] [PID:4079] [RANK:0] patching _expand_mask\u001b[39m\n",
-            "[2024-01-27 22:10:45,770] [INFO] [axolotl.load_model:641] [PID:4079] [RANK:0] GPU memory usage after model load: 2.062GB (+0.087GB cache, +0.352GB misc)\u001b[39m\n",
-            "[2024-01-27 22:10:45,787] [INFO] [axolotl.load_model:685] [PID:4079] [RANK:0] converting modules to torch.float16 for flash attention\u001b[39m\n",
-            "[2024-01-27 22:10:45,791] [INFO] [axolotl.load_lora:797] [PID:4079] [RANK:0] found linear modules: ['q_proj', 'down_proj', 'v_proj', 'gate_proj', 'k_proj', 'up_proj', 'o_proj']\u001b[39m\n",
-            "[2024-01-27 22:10:45,791] [DEBUG] [axolotl.load_lora:816] [PID:4079] [RANK:0] Loading pretained PEFT - LoRA\u001b[39m\n",
-            "trainable params: 25,231,360 || all params: 1,125,279,744 || trainable%: 2.2422299996542017\n",
-            "[2024-01-27 22:10:46,372] [INFO] [axolotl.load_model:722] [PID:4079] [RANK:0] GPU memory usage after adapters: 2.590GB (+1.060GB cache, +0.368GB misc)\u001b[39m\n",
-            "[2024-01-27 22:10:46,372] [INFO] [axolotl.scripts.do_merge_lora:79] [PID:4079] [RANK:0] running merge of LoRA with base model\u001b[39m\n",
-            "Unloading and merging model: 100% 468/468 [00:00<00:00, 4211.50it/s]\n",
-            "[2024-01-27 22:10:46,490] [INFO] [axolotl.scripts.do_merge_lora:88] [PID:4079] [RANK:0] saving merged model to: qlora-out/merged\u001b[39m\n",
-            "\u001b[0m"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from huggingface_hub import HfApi\n",
-        "from google.colab import userdata\n",
-        "\n",
-        "new_model = \"mlabonne/TinyAlpaca\"\n",
-        "\n",
-        "# HF_TOKEN defined in the secrets tab in Google Colab\n",
-        "api = HfApi()\n",
-        "\n",
-        "# Upload merge folder\n",
-        "api.create_repo(\n",
-        "    repo_id=new_model,\n",
-        "    repo_type=\"model\",\n",
-        "    exist_ok=True,\n",
-        ")\n",
-        "api.upload_folder(\n",
-        "    repo_id=new_model,\n",
-        "    folder_path=\"qlora-out/merged\",\n",
-        ")"
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-              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
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-        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Fine_tune_Llama_2_in_Google_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
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-      "source": [
-        "# Fine-tune Llama 2 in Google Colab\n",
-        "> 🗣️ Large Language Model Course\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne), based on Younes Belkada's [GitHub Gist](https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da). Special thanks to Tolga HOŞGÖR for his solution to empty the VRAM.\n",
-        "\n",
-        "This notebook runs on a T4 GPU. (Last update: 01 Aug 2023)\n"
-      ],
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-      "execution_count": null,
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-      "source": [
-        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import os\n",
-        "import torch\n",
-        "from datasets import load_dataset\n",
-        "from transformers import (\n",
-        "    AutoModelForCausalLM,\n",
-        "    AutoTokenizer,\n",
-        "    BitsAndBytesConfig,\n",
-        "    HfArgumentParser,\n",
-        "    TrainingArguments,\n",
-        "    pipeline,\n",
-        "    logging,\n",
-        ")\n",
-        "from peft import LoraConfig, PeftModel\n",
-        "from trl import SFTTrainer"
-      ],
-      "metadata": {
-        "id": "nAMzy_0FtaUZ"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# The model that you want to train from the Hugging Face hub\n",
-        "model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
-        "\n",
-        "# The instruction dataset to use\n",
-        "dataset_name = \"mlabonne/guanaco-llama2-1k\"\n",
-        "\n",
-        "# Fine-tuned model name\n",
-        "new_model = \"llama-2-7b-miniguanaco\"\n",
-        "\n",
-        "################################################################################\n",
-        "# QLoRA parameters\n",
-        "################################################################################\n",
-        "\n",
-        "# LoRA attention dimension\n",
-        "lora_r = 64\n",
-        "\n",
-        "# Alpha parameter for LoRA scaling\n",
-        "lora_alpha = 16\n",
-        "\n",
-        "# Dropout probability for LoRA layers\n",
-        "lora_dropout = 0.1\n",
-        "\n",
-        "################################################################################\n",
-        "# bitsandbytes parameters\n",
-        "################################################################################\n",
-        "\n",
-        "# Activate 4-bit precision base model loading\n",
-        "use_4bit = True\n",
-        "\n",
-        "# Compute dtype for 4-bit base models\n",
-        "bnb_4bit_compute_dtype = \"float16\"\n",
-        "\n",
-        "# Quantization type (fp4 or nf4)\n",
-        "bnb_4bit_quant_type = \"nf4\"\n",
-        "\n",
-        "# Activate nested quantization for 4-bit base models (double quantization)\n",
-        "use_nested_quant = False\n",
-        "\n",
-        "################################################################################\n",
-        "# TrainingArguments parameters\n",
-        "################################################################################\n",
-        "\n",
-        "# Output directory where the model predictions and checkpoints will be stored\n",
-        "output_dir = \"./results\"\n",
-        "\n",
-        "# Number of training epochs\n",
-        "num_train_epochs = 1\n",
-        "\n",
-        "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
-        "fp16 = False\n",
-        "bf16 = False\n",
-        "\n",
-        "# Batch size per GPU for training\n",
-        "per_device_train_batch_size = 4\n",
-        "\n",
-        "# Batch size per GPU for evaluation\n",
-        "per_device_eval_batch_size = 4\n",
-        "\n",
-        "# Number of update steps to accumulate the gradients for\n",
-        "gradient_accumulation_steps = 1\n",
-        "\n",
-        "# Enable gradient checkpointing\n",
-        "gradient_checkpointing = True\n",
-        "\n",
-        "# Maximum gradient normal (gradient clipping)\n",
-        "max_grad_norm = 0.3\n",
-        "\n",
-        "# Initial learning rate (AdamW optimizer)\n",
-        "learning_rate = 2e-4\n",
-        "\n",
-        "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
-        "weight_decay = 0.001\n",
-        "\n",
-        "# Optimizer to use\n",
-        "optim = \"paged_adamw_32bit\"\n",
-        "\n",
-        "# Learning rate schedule\n",
-        "lr_scheduler_type = \"cosine\"\n",
-        "\n",
-        "# Number of training steps (overrides num_train_epochs)\n",
-        "max_steps = -1\n",
-        "\n",
-        "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
-        "warmup_ratio = 0.03\n",
-        "\n",
-        "# Group sequences into batches with same length\n",
-        "# Saves memory and speeds up training considerably\n",
-        "group_by_length = True\n",
-        "\n",
-        "# Save checkpoint every X updates steps\n",
-        "save_steps = 0\n",
-        "\n",
-        "# Log every X updates steps\n",
-        "logging_steps = 25\n",
-        "\n",
-        "################################################################################\n",
-        "# SFT parameters\n",
-        "################################################################################\n",
-        "\n",
-        "# Maximum sequence length to use\n",
-        "max_seq_length = None\n",
-        "\n",
-        "# Pack multiple short examples in the same input sequence to increase efficiency\n",
-        "packing = False\n",
-        "\n",
-        "# Load the entire model on the GPU 0\n",
-        "device_map = {\"\": 0}"
-      ],
-      "metadata": {
-        "id": "ib_We3NLtj2E"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Load dataset (you can process it here)\n",
-        "dataset = load_dataset(dataset_name, split=\"train\")\n",
-        "\n",
-        "# Load tokenizer and model with QLoRA configuration\n",
-        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
-        "\n",
-        "bnb_config = BitsAndBytesConfig(\n",
-        "    load_in_4bit=use_4bit,\n",
-        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
-        "    bnb_4bit_compute_dtype=compute_dtype,\n",
-        "    bnb_4bit_use_double_quant=use_nested_quant,\n",
-        ")\n",
-        "\n",
-        "# Check GPU compatibility with bfloat16\n",
-        "if compute_dtype == torch.float16 and use_4bit:\n",
-        "    major, _ = torch.cuda.get_device_capability()\n",
-        "    if major >= 8:\n",
-        "        print(\"=\" * 80)\n",
-        "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
-        "        print(\"=\" * 80)\n",
-        "\n",
-        "# Load base model\n",
-        "model = AutoModelForCausalLM.from_pretrained(\n",
-        "    model_name,\n",
-        "    quantization_config=bnb_config,\n",
-        "    device_map=device_map\n",
-        ")\n",
-        "model.config.use_cache = False\n",
-        "model.config.pretraining_tp = 1\n",
-        "\n",
-        "# Load LLaMA tokenizer\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
-        "tokenizer.pad_token = tokenizer.eos_token\n",
-        "tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\n",
-        "\n",
-        "# Load LoRA configuration\n",
-        "peft_config = LoraConfig(\n",
-        "    lora_alpha=lora_alpha,\n",
-        "    lora_dropout=lora_dropout,\n",
-        "    r=lora_r,\n",
-        "    bias=\"none\",\n",
-        "    task_type=\"CAUSAL_LM\",\n",
-        ")\n",
-        "\n",
-        "# Set training parameters\n",
-        "training_arguments = TrainingArguments(\n",
-        "    output_dir=output_dir,\n",
-        "    num_train_epochs=num_train_epochs,\n",
-        "    per_device_train_batch_size=per_device_train_batch_size,\n",
-        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
-        "    optim=optim,\n",
-        "    save_steps=save_steps,\n",
-        "    logging_steps=logging_steps,\n",
-        "    learning_rate=learning_rate,\n",
-        "    weight_decay=weight_decay,\n",
-        "    fp16=fp16,\n",
-        "    bf16=bf16,\n",
-        "    max_grad_norm=max_grad_norm,\n",
-        "    max_steps=max_steps,\n",
-        "    warmup_ratio=warmup_ratio,\n",
-        "    group_by_length=group_by_length,\n",
-        "    lr_scheduler_type=lr_scheduler_type,\n",
-        "    report_to=\"tensorboard\"\n",
-        ")\n",
-        "\n",
-        "# Set supervised fine-tuning parameters\n",
-        "trainer = SFTTrainer(\n",
-        "    model=model,\n",
-        "    train_dataset=dataset,\n",
-        "    peft_config=peft_config,\n",
-        "    dataset_text_field=\"text\",\n",
-        "    max_seq_length=max_seq_length,\n",
-        "    tokenizer=tokenizer,\n",
-        "    args=training_arguments,\n",
-        "    packing=packing,\n",
-        ")\n",
-        "\n",
-        "# Train model\n",
-        "trainer.train()\n",
-        "\n",
-        "# Save trained model\n",
-        "trainer.model.save_pretrained(new_model)"
-      ],
-      "metadata": {
-        "id": "OJXpOgBFuSrc",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000,
-          "referenced_widgets": [
-            "52c4bf7418f74bc79a8c12fe35901974",
-            "c5e609d111b34d408a53a4cd71bb43d5",
-            "0e0a20b5ed7a44e9834022e7eba2194d",
-            "b5627331e78e4eb28765ed20f32cf403",
-            "8084d4cb267f4a52b3d80ec34d291190",
-            "a8dcdf1f7ab64242acb057e8b54ebf79",
-            "1ca492fddbaa4ea7a3226649154e01fd",
-            "a8eda8bfe08e4152a80c63830138c96d",
-            "1f258eacd6d0472385d41523b65dea8b",
-            "228b1bcf604f454f8060a250b58008a1",
-            "90b281e9c5ed4e77ab93e5879d0b15a3"
-          ]
-        },
-        "outputId": "8d06ed40-ea32-4d85-8665-413bde069607"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "52c4bf7418f74bc79a8c12fe35901974"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
-            "  warnings.warn(\n",
-            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:159: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
-            "  warnings.warn(\n",
-            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [250/250 24:05, Epoch 1/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>1</td>\n",
-              "      <td>1.350100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2</td>\n",
-              "      <td>2.015800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>3</td>\n",
-              "      <td>1.048700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>4</td>\n",
-              "      <td>1.287700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>5</td>\n",
-              "      <td>1.451200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>6</td>\n",
-              "      <td>1.659900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>7</td>\n",
-              "      <td>1.472300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>8</td>\n",
-              "      <td>1.326700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>9</td>\n",
-              "      <td>1.140000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>10</td>\n",
-              "      <td>1.395300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>11</td>\n",
-              "      <td>1.776400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>12</td>\n",
-              "      <td>1.169100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>13</td>\n",
-              "      <td>1.434700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>14</td>\n",
-              "      <td>1.550400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>15</td>\n",
-              "      <td>1.440400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>16</td>\n",
-              "      <td>1.352100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>17</td>\n",
-              "      <td>1.062800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>18</td>\n",
-              "      <td>1.173400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>19</td>\n",
-              "      <td>1.385300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>20</td>\n",
-              "      <td>1.433300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>21</td>\n",
-              "      <td>1.787800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>22</td>\n",
-              "      <td>1.600200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>23</td>\n",
-              "      <td>1.067800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>24</td>\n",
-              "      <td>1.679300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>25</td>\n",
-              "      <td>1.209900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>26</td>\n",
-              "      <td>1.305200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>27</td>\n",
-              "      <td>1.465300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>28</td>\n",
-              "      <td>1.781800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>29</td>\n",
-              "      <td>1.152400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>30</td>\n",
-              "      <td>1.434400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>31</td>\n",
-              "      <td>1.399300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>32</td>\n",
-              "      <td>1.796300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>33</td>\n",
-              "      <td>1.674500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>34</td>\n",
-              "      <td>1.567600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>35</td>\n",
-              "      <td>1.830000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>36</td>\n",
-              "      <td>1.720200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>37</td>\n",
-              "      <td>1.335800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>38</td>\n",
-              "      <td>1.333000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>39</td>\n",
-              "      <td>2.044900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>40</td>\n",
-              "      <td>1.832200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>41</td>\n",
-              "      <td>1.533900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>42</td>\n",
-              "      <td>1.259900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>43</td>\n",
-              "      <td>1.372300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>44</td>\n",
-              "      <td>1.551600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>45</td>\n",
-              "      <td>2.002400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>46</td>\n",
-              "      <td>1.956100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>47</td>\n",
-              "      <td>2.441900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>48</td>\n",
-              "      <td>2.289100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>49</td>\n",
-              "      <td>1.544500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>50</td>\n",
-              "      <td>2.040300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>51</td>\n",
-              "      <td>1.103800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>52</td>\n",
-              "      <td>1.630800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>53</td>\n",
-              "      <td>1.437900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>54</td>\n",
-              "      <td>1.820900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>55</td>\n",
-              "      <td>1.080300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>56</td>\n",
-              "      <td>1.029200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>57</td>\n",
-              "      <td>0.999400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>58</td>\n",
-              "      <td>0.795900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>59</td>\n",
-              "      <td>1.331600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>60</td>\n",
-              "      <td>1.099500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>61</td>\n",
-              "      <td>1.199000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>62</td>\n",
-              "      <td>1.146000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>63</td>\n",
-              "      <td>1.129000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>64</td>\n",
-              "      <td>1.109500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>65</td>\n",
-              "      <td>1.207000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>66</td>\n",
-              "      <td>1.360600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>67</td>\n",
-              "      <td>1.879000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>68</td>\n",
-              "      <td>1.317200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>69</td>\n",
-              "      <td>1.033300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>70</td>\n",
-              "      <td>1.153400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>71</td>\n",
-              "      <td>1.112400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>72</td>\n",
-              "      <td>1.218400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>73</td>\n",
-              "      <td>1.134600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>74</td>\n",
-              "      <td>1.053200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>75</td>\n",
-              "      <td>1.008900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>76</td>\n",
-              "      <td>1.077000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>77</td>\n",
-              "      <td>1.245000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>78</td>\n",
-              "      <td>1.395900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>79</td>\n",
-              "      <td>1.488800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>80</td>\n",
-              "      <td>1.382500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>81</td>\n",
-              "      <td>1.442200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>82</td>\n",
-              "      <td>1.028500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>83</td>\n",
-              "      <td>1.208500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>84</td>\n",
-              "      <td>1.780200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>85</td>\n",
-              "      <td>1.679300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>86</td>\n",
-              "      <td>1.276600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>87</td>\n",
-              "      <td>1.374600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>88</td>\n",
-              "      <td>1.490000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>89</td>\n",
-              "      <td>1.567100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>90</td>\n",
-              "      <td>1.435000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>91</td>\n",
-              "      <td>1.329800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>92</td>\n",
-              "      <td>1.387600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>93</td>\n",
-              "      <td>0.971400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>94</td>\n",
-              "      <td>1.293800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>95</td>\n",
-              "      <td>1.585900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>96</td>\n",
-              "      <td>1.431700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>97</td>\n",
-              "      <td>1.948900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>98</td>\n",
-              "      <td>1.630500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>99</td>\n",
-              "      <td>1.839100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>100</td>\n",
-              "      <td>1.740900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>101</td>\n",
-              "      <td>0.717200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>102</td>\n",
-              "      <td>0.958100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>103</td>\n",
-              "      <td>1.625900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>104</td>\n",
-              "      <td>1.150000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>105</td>\n",
-              "      <td>0.999200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>106</td>\n",
-              "      <td>1.253100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>107</td>\n",
-              "      <td>1.007600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>108</td>\n",
-              "      <td>1.049700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>109</td>\n",
-              "      <td>1.265900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>110</td>\n",
-              "      <td>1.251300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>111</td>\n",
-              "      <td>1.109500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>112</td>\n",
-              "      <td>1.652500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>113</td>\n",
-              "      <td>1.238000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>114</td>\n",
-              "      <td>1.521300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>115</td>\n",
-              "      <td>1.002400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>116</td>\n",
-              "      <td>0.982400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>117</td>\n",
-              "      <td>1.389300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>118</td>\n",
-              "      <td>1.114900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>119</td>\n",
-              "      <td>1.093900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>120</td>\n",
-              "      <td>1.254200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>121</td>\n",
-              "      <td>1.132300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>122</td>\n",
-              "      <td>0.925300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>123</td>\n",
-              "      <td>1.292700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>124</td>\n",
-              "      <td>1.317600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>125</td>\n",
-              "      <td>1.080400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>126</td>\n",
-              "      <td>0.918800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>127</td>\n",
-              "      <td>1.203400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>128</td>\n",
-              "      <td>1.098800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>129</td>\n",
-              "      <td>1.360800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>130</td>\n",
-              "      <td>1.256900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>131</td>\n",
-              "      <td>1.392600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>132</td>\n",
-              "      <td>1.167600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>133</td>\n",
-              "      <td>1.134900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>134</td>\n",
-              "      <td>1.423700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>135</td>\n",
-              "      <td>1.111200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>136</td>\n",
-              "      <td>1.081600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>137</td>\n",
-              "      <td>1.806000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>138</td>\n",
-              "      <td>1.238800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>139</td>\n",
-              "      <td>1.306800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>140</td>\n",
-              "      <td>1.421900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>141</td>\n",
-              "      <td>1.467300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>142</td>\n",
-              "      <td>1.245100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>143</td>\n",
-              "      <td>1.594200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>144</td>\n",
-              "      <td>1.426000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>145</td>\n",
-              "      <td>1.393800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>146</td>\n",
-              "      <td>1.894400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>147</td>\n",
-              "      <td>1.331200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>148</td>\n",
-              "      <td>1.519400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>149</td>\n",
-              "      <td>1.926300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>150</td>\n",
-              "      <td>1.293200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>151</td>\n",
-              "      <td>1.135100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>152</td>\n",
-              "      <td>1.066700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>153</td>\n",
-              "      <td>0.856900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>154</td>\n",
-              "      <td>1.021500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>155</td>\n",
-              "      <td>0.808800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>156</td>\n",
-              "      <td>0.936300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>157</td>\n",
-              "      <td>0.979700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>158</td>\n",
-              "      <td>1.100200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>159</td>\n",
-              "      <td>1.091400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>160</td>\n",
-              "      <td>0.918800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>161</td>\n",
-              "      <td>1.370800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>162</td>\n",
-              "      <td>1.380300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>163</td>\n",
-              "      <td>0.965300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>164</td>\n",
-              "      <td>1.142400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>165</td>\n",
-              "      <td>1.436400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>166</td>\n",
-              "      <td>0.970400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>167</td>\n",
-              "      <td>0.872600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>168</td>\n",
-              "      <td>1.662500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>169</td>\n",
-              "      <td>1.623500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>170</td>\n",
-              "      <td>1.481700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>171</td>\n",
-              "      <td>0.822300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>172</td>\n",
-              "      <td>1.605500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>173</td>\n",
-              "      <td>1.769800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>174</td>\n",
-              "      <td>1.320100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>175</td>\n",
-              "      <td>0.969300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>176</td>\n",
-              "      <td>0.798700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>177</td>\n",
-              "      <td>1.233200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>178</td>\n",
-              "      <td>1.168500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>179</td>\n",
-              "      <td>1.251400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>180</td>\n",
-              "      <td>1.221500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>181</td>\n",
-              "      <td>1.491100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>182</td>\n",
-              "      <td>1.010200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>183</td>\n",
-              "      <td>1.375500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>184</td>\n",
-              "      <td>1.722900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>185</td>\n",
-              "      <td>1.179300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>186</td>\n",
-              "      <td>1.474400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>187</td>\n",
-              "      <td>1.968200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>188</td>\n",
-              "      <td>1.297200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>189</td>\n",
-              "      <td>1.564500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>190</td>\n",
-              "      <td>1.480700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>191</td>\n",
-              "      <td>1.464700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>192</td>\n",
-              "      <td>1.901400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>193</td>\n",
-              "      <td>1.620100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>194</td>\n",
-              "      <td>1.509000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>195</td>\n",
-              "      <td>1.587000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>196</td>\n",
-              "      <td>1.510000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>197</td>\n",
-              "      <td>1.773900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>198</td>\n",
-              "      <td>1.473200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>199</td>\n",
-              "      <td>1.660400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>200</td>\n",
-              "      <td>1.832600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>201</td>\n",
-              "      <td>1.021400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>202</td>\n",
-              "      <td>1.120400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>203</td>\n",
-              "      <td>1.030200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>204</td>\n",
-              "      <td>1.167500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>205</td>\n",
-              "      <td>0.853200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>206</td>\n",
-              "      <td>0.927000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>207</td>\n",
-              "      <td>1.157400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>208</td>\n",
-              "      <td>1.071600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>209</td>\n",
-              "      <td>1.195400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>210</td>\n",
-              "      <td>1.155800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>211</td>\n",
-              "      <td>1.502300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>212</td>\n",
-              "      <td>1.091600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>213</td>\n",
-              "      <td>1.225200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>214</td>\n",
-              "      <td>1.148900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>215</td>\n",
-              "      <td>1.238200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>216</td>\n",
-              "      <td>1.600200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>217</td>\n",
-              "      <td>1.203600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>218</td>\n",
-              "      <td>1.266200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>219</td>\n",
-              "      <td>0.970900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>220</td>\n",
-              "      <td>1.451000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>221</td>\n",
-              "      <td>1.281300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>222</td>\n",
-              "      <td>0.952500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>223</td>\n",
-              "      <td>1.313800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>224</td>\n",
-              "      <td>0.915700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>225</td>\n",
-              "      <td>1.040000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>226</td>\n",
-              "      <td>1.493800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>227</td>\n",
-              "      <td>1.186400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>228</td>\n",
-              "      <td>1.278700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>229</td>\n",
-              "      <td>1.061100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>230</td>\n",
-              "      <td>1.209000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>231</td>\n",
-              "      <td>0.881400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>232</td>\n",
-              "      <td>1.659300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>233</td>\n",
-              "      <td>1.135200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>234</td>\n",
-              "      <td>1.497800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>235</td>\n",
-              "      <td>1.557500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>236</td>\n",
-              "      <td>0.849200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>237</td>\n",
-              "      <td>1.329200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>238</td>\n",
-              "      <td>1.147700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>239</td>\n",
-              "      <td>1.764600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>240</td>\n",
-              "      <td>1.740000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>241</td>\n",
-              "      <td>2.043700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>242</td>\n",
-              "      <td>1.675000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>243</td>\n",
-              "      <td>1.809600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>244</td>\n",
-              "      <td>1.721400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>245</td>\n",
-              "      <td>2.343300</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>246</td>\n",
-              "      <td>1.830400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>247</td>\n",
-              "      <td>1.754400</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>248</td>\n",
-              "      <td>1.741900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>249</td>\n",
-              "      <td>2.011000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>250</td>\n",
-              "      <td>1.741700</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# %load_ext tensorboard\n",
-        "# %tensorboard --logdir results/runs"
-      ],
-      "metadata": {
-        "id": "crj9svNe4hU5"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Ignore warnings\n",
-        "logging.set_verbosity(logging.CRITICAL)\n",
-        "\n",
-        "# Run text generation pipeline with our next model\n",
-        "prompt = \"What is a large language model?\"\n",
-        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
-        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
-        "print(result[0]['generated_text'])"
-      ],
-      "metadata": {
-        "id": "frlSLPin4IJ4",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "e5bf6b3a-f20e-49f7-e0b7-36f71ca207c1"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
-            "  warnings.warn(\n",
-            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
-            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<s>[INST] What is a large language model? [/INST] A large language model is a type of artificial intelligence (AI) model that is trained on a large dataset of text to generate human-like language outputs. It is designed to be able to understand and generate text in a way that is similar to human language, and can be used for a wide range of applications such as chatbots, language translation, and text summarization.\n",
-            "\n",
-            "Large language models are typically trained using deep learning techniques, such as recurrent neural networks (RNNs) or transformer models, and are often based on pre-trained models such as BERT or RoBERTa. These models are trained on large datasets of text, such as books, articles, or websites, and are designed to learn the patterns and structures of language.\n",
-            "\n",
-            "Some examples of large language models include:\n",
-            "\n",
-            "* BERT (Bidirectional Encoder Representations from Transformers\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Empty VRAM\n",
-        "del model\n",
-        "del pipe\n",
-        "del trainer\n",
-        "import gc\n",
-        "gc.collect()\n",
-        "gc.collect()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mkQCviG0Zta-",
-        "outputId": "e7c4ab10-4039-4490-b7f0-6ea118bdd709"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "19965"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 7
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Reload model in FP16 and merge it with LoRA weights\n",
-        "base_model = AutoModelForCausalLM.from_pretrained(\n",
-        "    model_name,\n",
-        "    low_cpu_mem_usage=True,\n",
-        "    return_dict=True,\n",
-        "    torch_dtype=torch.float16,\n",
-        "    device_map=device_map,\n",
-        ")\n",
-        "model = PeftModel.from_pretrained(base_model, new_model)\n",
-        "model = model.merge_and_unload()\n",
-        "\n",
-        "# Reload tokenizer to save it\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
-        "tokenizer.pad_token = tokenizer.eos_token\n",
-        "tokenizer.padding_side = \"right\""
-      ],
-      "metadata": {
-        "id": "QQn30cRtAZ-P",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 49,
-          "referenced_widgets": [
-            "051d193cd87f47c1971fb87544e1e615",
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-            "d95f3a3f26c6470d984542cdfd68bec1",
-            "343e11c62a59448eb43bbc0c31bf5f11",
-            "a153c96bd1fe4c48a41e9b9c7c00dd6e",
-            "84da055d24694320843e13ad37438792",
-            "e375632975904402baea46163e2eeca1",
-            "95501d0b5a22407288f008bf8cc69726",
-            "6aef866a6c474dfabb2140ded933c5aa",
-            "d66fa096d442423c9447cbfbdc1aad8d"
-          ]
-        },
-        "outputId": "1c5ef3c4-d107-4c43-9bd6-0ca72903db0e"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "051d193cd87f47c1971fb87544e1e615"
-            }
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!huggingface-cli login\n",
-        "\n",
-        "model.push_to_hub(new_model, use_temp_dir=False)\n",
-        "tokenizer.push_to_hub(new_model, use_temp_dir=False)"
-      ],
-      "metadata": {
-        "id": "x-xPb-_qB0dz",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 373,
-          "referenced_widgets": [
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-            "e4698337e6b843afac706ab657ca6af9",
-            "1af01f1f1aac42b8bff46fe4df8a59ad",
-            "eee8731f316244eda5ff0765fd12bf85",
-            "f135278e410f4b708435bb80fb630bcf",
-            "2e6fc79bf5c149d6b0bc5c52e18debc7",
-            "a4b0debc025444a59abd6953b3512c0d",
-            "130120644beb48acbc038651459af43c",
-            "bf77e97593a349718bdb5fd9bfd28fe3",
-            "f7292741953e47699540ef8712fc0d8d",
-            "9434350b1b9c4060812feb9ecbf63278",
-            "b29647e268414329be56047e522e28b9",
-            "27bb18a199ca47108c7a61e9c443de36",
-            "33ebb868f3e846f6af1a1a2a8ad6a3cb",
-            "1f73f8b4d4da4e74adc135f2a2f6ee65",
-            "68da6e6e69c8419895bea2068760534e",
-            "6dc1a868e08c4c3b8315116d2c46573b",
-            "7a5d714c17374104bb6f5caaa5541c10",
-            "1b6c59a51359453c926bfcddb3d0f0ea",
-            "dac3669f18284161a58d52f26dffb761",
-            "a3511f489f6d47cc8d404ab6f367b29f",
-            "20670478612f4b1a8a5f23d71a2609a7",
-            "b463153ec04749e38540389efa2981f7",
-            "2bb3d36d248a48fba364f14d9e840306"
-          ]
-        },
-        "outputId": "6ed9166c-5f92-4375-eca5-dbb247c0e13a"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\n",
-            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
-            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
-            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
-            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
-            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
-            "    \n",
-            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
-            "Token: \n",
-            "Add token as git credential? (Y/n) n\n",
-            "Token is valid (permission: write).\n",
-            "Your token has been saved to /root/.cache/huggingface/token\n",
-            "Login successful\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "c99aff4cfd664ae8a165a27bea0566c8"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "pytorch_model-00001-of-00002.bin:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "1af01f1f1aac42b8bff46fe4df8a59ad"
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-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "pytorch_model-00002-of-00002.bin:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "33ebb868f3e846f6af1a1a2a8ad6a3cb"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "CommitInfo(commit_url='https://huggingface.co/mlabonne/llama-2-7b-miniguanaco/commit/c81a32fd0b4d39e252326e639d63e75aa68c9a4a', commit_message='Upload tokenizer', commit_description='', oid='c81a32fd0b4d39e252326e639d63e75aa68c9a4a', pr_url=None, pr_revision=None, pr_num=None)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 10
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/Fine_tune_a_Mistral_7b_model_with_DPO.ipynb b/Fine_tune_a_Mistral_7b_model_with_DPO.ipynb
deleted file mode 100644
index 788a43c..0000000
--- a/Fine_tune_a_Mistral_7b_model_with_DPO.ipynb
+++ /dev/null
@@ -1,757 +0,0 @@
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-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Fine_tune_a_Mistral_7b_model_with_DPO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Fine-tune a Mistral-7b model with DPO\n",
-        "\n",
-        "> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne)."
-      ],
-      "metadata": {
-        "id": "Pa8905-YsHAn"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_zIBL8IssExG"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q datasets trl peft bitsandbytes sentencepiece wandb"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import os\n",
-        "import gc\n",
-        "import torch\n",
-        "\n",
-        "import transformers\n",
-        "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig\n",
-        "from datasets import load_dataset\n",
-        "from peft import LoraConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training\n",
-        "from trl import DPOTrainer\n",
-        "import bitsandbytes as bnb\n",
-        "from google.colab import userdata\n",
-        "import wandb\n",
-        "\n",
-        "# Defined in the secrets tab in Google Colab\n",
-        "hf_token = userdata.get('huggingface')\n",
-        "wb_token = userdata.get('wandb')\n",
-        "wandb.login(key=wb_token)\n",
-        "\n",
-        "model_name = \"teknium/OpenHermes-2.5-Mistral-7B\"\n",
-        "new_model = \"NeuralHermes-2.5-Mistral-7B\""
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "YpdkZsMNylvp",
-        "outputId": "6c2df234-1ce7-4cd2-a7e3-567e7536319f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n",
-            "  warnings.warn(\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmlabonne\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Format dataset"
-      ],
-      "metadata": {
-        "id": "d8CvUgROUDw-"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def chatml_format(example):\n",
-        "    # Format system\n",
-        "    if len(example['system']) > 0:\n",
-        "        message = {\"role\": \"system\", \"content\": example['system']}\n",
-        "        system = tokenizer.apply_chat_template([message], tokenize=False)\n",
-        "    else:\n",
-        "        system = \"\"\n",
-        "\n",
-        "    # Format instruction\n",
-        "    message = {\"role\": \"user\", \"content\": example['question']}\n",
-        "    prompt = tokenizer.apply_chat_template([message], tokenize=False, add_generation_prompt=True)\n",
-        "\n",
-        "    # Format chosen answer\n",
-        "    chosen = example['chosen'] + \"<|im_end|>\\n\"\n",
-        "\n",
-        "    # Format rejected answer\n",
-        "    rejected = example['rejected'] + \"<|im_end|>\\n\"\n",
-        "\n",
-        "    return {\n",
-        "        \"prompt\": system + prompt,\n",
-        "        \"chosen\": chosen,\n",
-        "        \"rejected\": rejected,\n",
-        "    }\n",
-        "\n",
-        "# Load dataset\n",
-        "dataset = load_dataset(\"Intel/orca_dpo_pairs\")['train']\n",
-        "\n",
-        "# Save columns\n",
-        "original_columns = dataset.column_names\n",
-        "\n",
-        "# Tokenizer\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-        "tokenizer.pad_token = tokenizer.eos_token\n",
-        "tokenizer.padding_side = \"left\"\n",
-        "\n",
-        "# Format dataset\n",
-        "dataset = dataset.map(\n",
-        "    chatml_format,\n",
-        "    remove_columns=original_columns\n",
-        ")\n",
-        "\n",
-        "# Print sample\n",
-        "dataset[1]"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "MCD77GZ60DOT",
-        "outputId": "c7c6773c-5545-4fee-bfa3-6fa6d69c0f3f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
-            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'prompt': '<|im_start|>system\\nYou are an AI assistant. You will be given a task. You must generate a detailed and long answer.<|im_end|>\\n<|im_start|>user\\nGenerate an approximately fifteen-word sentence that describes all this data: Midsummer House eatType restaurant; Midsummer House food Chinese; Midsummer House priceRange moderate; Midsummer House customer rating 3 out of 5; Midsummer House near All Bar One<|im_end|>\\n<|im_start|>assistant\\n',\n",
-              " 'chosen': 'Midsummer House is a moderately priced Chinese restaurant with a 3/5 customer rating, located near All Bar One.<|im_end|>\\n',\n",
-              " 'rejected': ' Sure! Here\\'s a sentence that describes all the data you provided:\\n\\n\"Midsummer House is a moderately priced Chinese restaurant with a customer rating of 3 out of 5, located near All Bar One, offering a variety of delicious dishes.\"<|im_end|>\\n'}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Train model with DPO"
-      ],
-      "metadata": {
-        "id": "DeT5eUK_UJgK"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# LoRA configuration\n",
-        "peft_config = LoraConfig(\n",
-        "    r=16,\n",
-        "    lora_alpha=16,\n",
-        "    lora_dropout=0.05,\n",
-        "    bias=\"none\",\n",
-        "    task_type=\"CAUSAL_LM\",\n",
-        "    target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']\n",
-        ")\n",
-        "\n",
-        "# Model to fine-tune\n",
-        "model = AutoModelForCausalLM.from_pretrained(\n",
-        "    model_name,\n",
-        "    torch_dtype=torch.float16,\n",
-        "    load_in_4bit=True\n",
-        ")\n",
-        "model.config.use_cache = False\n",
-        "\n",
-        "# Training arguments\n",
-        "training_args = TrainingArguments(\n",
-        "    per_device_train_batch_size=4,\n",
-        "    gradient_accumulation_steps=4,\n",
-        "    gradient_checkpointing=True,\n",
-        "    learning_rate=5e-5,\n",
-        "    lr_scheduler_type=\"cosine\",\n",
-        "    max_steps=200,\n",
-        "    save_strategy=\"no\",\n",
-        "    logging_steps=1,\n",
-        "    output_dir=new_model,\n",
-        "    optim=\"paged_adamw_32bit\",\n",
-        "    warmup_steps=100,\n",
-        "    bf16=True,\n",
-        "    report_to=\"wandb\",\n",
-        ")\n",
-        "\n",
-        "# Create DPO trainer\n",
-        "dpo_trainer = DPOTrainer(\n",
-        "    model,\n",
-        "    args=training_args,\n",
-        "    train_dataset=dataset,\n",
-        "    tokenizer=tokenizer,\n",
-        "    peft_config=peft_config,\n",
-        "    beta=0.1,\n",
-        "    max_prompt_length=1024,\n",
-        "    max_length=1536,\n",
-        ")\n",
-        "\n",
-        "# Fine-tune model with DPO\n",
-        "dpo_trainer.train()"
-      ],
-      "metadata": {
-        "id": "rKPILNOLR-aK"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Upload model"
-      ],
-      "metadata": {
-        "id": "3LdhPpcrUM3H"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Save artifacts\n",
-        "dpo_trainer.model.save_pretrained(\"final_checkpoint\")\n",
-        "tokenizer.save_pretrained(\"final_checkpoint\")\n",
-        "\n",
-        "# Flush memory\n",
-        "del dpo_trainer, model\n",
-        "gc.collect()\n",
-        "torch.cuda.empty_cache()\n",
-        "\n",
-        "# Reload model in FP16 (instead of NF4)\n",
-        "base_model = AutoModelForCausalLM.from_pretrained(\n",
-        "    model_name,\n",
-        "    return_dict=True,\n",
-        "    torch_dtype=torch.float16,\n",
-        ")\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-        "\n",
-        "# Merge base model with the adapter\n",
-        "model = PeftModel.from_pretrained(base_model, \"final_checkpoint\")\n",
-        "model = model.merge_and_unload()\n",
-        "\n",
-        "# Save model and tokenizer\n",
-        "model.save_pretrained(new_model)\n",
-        "tokenizer.save_pretrained(new_model)\n",
-        "\n",
-        "# Push them to the HF Hub\n",
-        "model.push_to_hub(new_model, use_temp_dir=False, token=hf_token)\n",
-        "tokenizer.push_to_hub(new_model, use_temp_dir=False, token=hf_token)"
-      ],
-      "metadata": {
-        "id": "h7cIvxcTfBC4"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Inference"
-      ],
-      "metadata": {
-        "id": "G6EFsmS4UOgV"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Format prompt\n",
-        "message = [\n",
-        "    {\"role\": \"system\", \"content\": \"You are a helpful assistant chatbot.\"},\n",
-        "    {\"role\": \"user\", \"content\": \"What is a Large Language Model?\"}\n",
-        "]\n",
-        "tokenizer = AutoTokenizer.from_pretrained(new_model)\n",
-        "prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)\n",
-        "\n",
-        "# Create pipeline\n",
-        "pipeline = transformers.pipeline(\n",
-        "    \"text-generation\",\n",
-        "    model=new_model,\n",
-        "    tokenizer=tokenizer\n",
-        ")\n",
-        "\n",
-        "# Generate text\n",
-        "sequences = pipeline(\n",
-        "    prompt,\n",
-        "    do_sample=True,\n",
-        "    temperature=0.7,\n",
-        "    top_p=0.9,\n",
-        "    num_return_sequences=1,\n",
-        "    max_length=200,\n",
-        ")\n",
-        "print(sequences[0]['generated_text'])"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 251,
-          "referenced_widgets": [
-            "22773c721a7c4221a9c14cd388461d4c",
-            "6b54841f5de1482694c360095dae3039",
-            "448ccbc85e624ec3b3e71931a7ee4ff6",
-            "173769f6f465485f8848a11bf269850b",
-            "60978b9b4e8348f0a71ce3e35c73bcff",
-            "6a38dcbaf4674b448329ac0a16587d2a",
-            "7eaeada2158e493189449af91f643553",
-            "6e32854952b340008edca0139d3471d6",
-            "db6d7cfcdade4b4baa213a5d0abc07d7",
-            "9083029642744c43b7705532cbe0cf79",
-            "d028a98caa13425b907ceb513119006e"
-          ]
-        },
-        "id": "LAEUZFjvlJOv",
-        "outputId": "9b5720c7-49ef-45c7-e5a7-f38d64899b1e"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "22773c721a7c4221a9c14cd388461d4c"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1473: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
-            "  warnings.warn(\n",
-            "Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<|im_start|>system\n",
-            "You are a helpful assistant chatbot.<|im_end|>\n",
-            "<|im_start|>user\n",
-            "What is a Large Language Model?<|im_end|>\n",
-            "<|im_start|>assistant\n",
-            "A large language model is a type of artificial intelligence (AI) system that has been trained on vast amounts of text data. These models are designed to understand and generate human language, allowing them to perform various natural language processing tasks, such as text generation, language translation, and question answering. Large language models typically use deep learning techniques, like recurrent neural networks (RNNs) or transformers, to learn patterns and relationships in the data, enabling them to generate coherent and contextually relevant responses. The size of these models, in terms of the number of parameters and the volume of data they are trained on, plays a significant role in their ability to comprehend and produce complex language structures.\n"
-          ]
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/Introduction_to_Weight_Quantization.ipynb b/Introduction_to_Weight_Quantization.ipynb
deleted file mode 100644
index 26225da..0000000
--- a/Introduction_to_Weight_Quantization.ipynb
+++ /dev/null
@@ -1,2822 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Introduction_to_Weight_Quantization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Introduction to Weight Quantization\n",
-        "> Reducing the size of Large Language Models with 8-bit quantization\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
-        "\n",
-        "Companion notebook to execute the code from the following article: https://mlabonne.github.io/blog/intro_weight_quantization/"
-      ],
-      "metadata": {
-        "id": "yG1VY-TJoxix"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "WMVwLxdUzlq2"
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n",
-        "\n",
-        "def absmax_quantize(X):\n",
-        "    # Calculate scale\n",
-        "    scale = 127 / torch.max(torch.abs(X))\n",
-        "\n",
-        "    # Quantize\n",
-        "    X_quant = (scale * X).round()\n",
-        "\n",
-        "    # Dequantize\n",
-        "    X_dequant = X_quant / scale\n",
-        "\n",
-        "    return X_quant.to(torch.int8), X_dequant"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "CE7XqWOR6oCa"
-      },
-      "outputs": [],
-      "source": [
-        "def zeropoint_quantize(X):\n",
-        "    # Calculate value range (denominator)\n",
-        "    x_range = torch.max(X) - torch.min(X)\n",
-        "    x_range = 1 if x_range == 0 else x_range\n",
-        "\n",
-        "    # Calculate scale\n",
-        "    scale = 255 / x_range\n",
-        "\n",
-        "    # Shift by zero-point\n",
-        "    zeropoint = (-scale * torch.min(X) - 128).round()\n",
-        "\n",
-        "    # Scale and round the inputs\n",
-        "    X_quant = torch.clip((X * scale + zeropoint).round(), -128, 127)\n",
-        "\n",
-        "    # Dequantize\n",
-        "    X_dequant = (X_quant - zeropoint) / scale\n",
-        "\n",
-        "    return X_quant.to(torch.int8), X_dequant"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "lIYdn1woOS1n"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q bitsandbytes>=0.39.0\n",
-        "!pip install -q git+https://github.com/huggingface/accelerate.git\n",
-        "!pip install -q git+https://github.com/huggingface/transformers.git"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 792,
-          "referenced_widgets": [
-            "e7e1636dbc8944c49b375286b8d89fe4",
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-            "d7364ae105524cfba0eed4095273ed57",
-            "eeb266ac1aba409184d9ac1fbd433e9c",
-            "fd1a537d4c1c49b6972f7bad58a24417",
-            "bb06d9a3f9f64821b7839ce777580c4b",
-            "c53b9dc02f144a91aeca032b8b67259b",
-            "e5a0a603b6ee48c8ab7e5f5bcb62c3ff",
-            "15a5a3eb178348ef83cb7d02198636b3",
-            "47b5db2b2caa48cca14ea162ed5e7bd3",
-            "7784bd5d3f1b4ac890a711c01bd653cb",
-            "7626f77200244f489150c2c4c6794e53",
-            "1482e979e3ae4731b9b673b5a7db44ab",
-            "d95190bba0364de99f04bb9adad173b5",
-            "2f4124fead964700a4158cf1373bf74e",
-            "6288899dd1c64a779d79e17095b8ea9d",
-            "c013211a09134f42934ac041f86b5cfe",
-            "85e096686a604059a0a61afacedac67b",
-            "0a23f8cda4484b6ca80c14b6691cc56d",
-            "dcb679577da0472c841ab985ef92618f",
-            "c7438dd71b964dea809e9f4a95046439",
-            "0e047a8a2a0e4337b17f3e8612044967",
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-            "fba8130749824daaa73c5890c773e900",
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-            "8d78e5c72f894275b504948461193b66",
-            "77b9f13345b24caaa7afc3bd43e33eba",
-            "57b079495f844af684c24bb1cd711bf0",
-            "0227537a636e41cb93abc9e416015cce",
-            "b45e6e8aa21f47c1821f2b24a2f46944",
-            "8257dc1927514846b51511bc387a54db",
-            "7e7d6bc9b8544e078003d2fe6c74dcef"
-          ]
-        },
-        "id": "NTDg7uUOGBmS",
-        "outputId": "cc48b090-31d1-41ae-ca5c-dbffcb67bcb6"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e7e1636dbc8944c49b375286b8d89fe4"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\n",
-            "===================================BUG REPORT===================================\n",
-            "Welcome to bitsandbytes. For bug reports, please run\n",
-            "\n",
-            "python -m bitsandbytes\n",
-            "\n",
-            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
-            "================================================================================\n",
-            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
-            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
-            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so.11.0\n",
-            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
-            "CUDA SETUP: Detected CUDA version 118\n",
-            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
-            "  warn(msg)\n",
-            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
-            "  warn(msg)\n",
-            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('//172.28.0.1'), PosixPath('8013')}\n",
-            "  warn(msg)\n",
-            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-20b5bv2xvtu9a --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
-            "  warn(msg)\n",
-            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
-            "  warn(msg)\n",
-            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
-            "  warn(msg)\n",
-            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
-            "Either way, this might cause trouble in the future:\n",
-            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
-            "  warn(msg)\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "7784bd5d3f1b4ac890a711c01bd653cb"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "0e047a8a2a0e4337b17f3e8612044967"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "681a9a78878945b7b6afb2d87b769146"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "cea83a47549a4ddb91eae020d1cd943c"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "90cf2d52029d4392aaea970508506261"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Model size: 510,342,192 bytes\n"
-          ]
-        }
-      ],
-      "source": [
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "import torch\n",
-        "torch.manual_seed(0)\n",
-        "\n",
-        "# Set device to CPU for now\n",
-        "device = 'cpu'\n",
-        "\n",
-        "# Load model and tokenizer\n",
-        "model_id = 'gpt2'\n",
-        "model = AutoModelForCausalLM.from_pretrained(model_id).to(device)\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
-        "\n",
-        "# Print model size\n",
-        "print(f\"Model size: {model.get_memory_footprint():,} bytes\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "YPI1EaimHyHm",
-        "outputId": "977e9b34-9426-46a1-d6c2-da80884b7483"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Original weights:\n",
-            "tensor([[-0.4738, -0.2614, -0.0978,  ...,  0.0513, -0.0584,  0.0250],\n",
-            "        [ 0.0874,  0.1473,  0.2387,  ..., -0.0525, -0.0113, -0.0156],\n",
-            "        [ 0.0039,  0.0695,  0.3668,  ...,  0.1143,  0.0363, -0.0318],\n",
-            "        ...,\n",
-            "        [-0.2592, -0.0164,  0.1991,  ...,  0.0095, -0.0516,  0.0319],\n",
-            "        [ 0.1517,  0.2170,  0.1043,  ...,  0.0293, -0.0429, -0.0475],\n",
-            "        [-0.4100, -0.1924, -0.2400,  ..., -0.0046,  0.0070,  0.0198]])\n",
-            "\n",
-            "Absmax quantized weights:\n",
-            "tensor([[-21, -12,  -4,  ...,   2,  -3,   1],\n",
-            "        [  4,   7,  11,  ...,  -2,  -1,  -1],\n",
-            "        [  0,   3,  16,  ...,   5,   2,  -1],\n",
-            "        ...,\n",
-            "        [-12,  -1,   9,  ...,   0,  -2,   1],\n",
-            "        [  7,  10,   5,  ...,   1,  -2,  -2],\n",
-            "        [-18,  -9, -11,  ...,   0,   0,   1]], dtype=torch.int8)\n",
-            "\n",
-            "Zero-point quantized weights:\n",
-            "tensor([[-20, -11,  -3,  ...,   3,  -2,   2],\n",
-            "        [  5,   8,  12,  ...,  -1,   0,   0],\n",
-            "        [  1,   4,  18,  ...,   6,   3,   0],\n",
-            "        ...,\n",
-            "        [-11,   0,  10,  ...,   1,  -1,   2],\n",
-            "        [  8,  11,   6,  ...,   2,  -1,  -1],\n",
-            "        [-18,  -8, -10,  ...,   1,   1,   2]], dtype=torch.int8)\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Extract weights of the first layer\n",
-        "weights = model.transformer.h[0].attn.c_attn.weight.data\n",
-        "print(\"Original weights:\")\n",
-        "print(weights)\n",
-        "\n",
-        "# Quantize layer using absmax quantization\n",
-        "weights_abs_quant, _ = absmax_quantize(weights)\n",
-        "print(\"\\nAbsmax quantized weights:\")\n",
-        "print(weights_abs_quant)\n",
-        "\n",
-        "# Quantize layer using absmax quantization\n",
-        "weights_zp_quant, _ = zeropoint_quantize(weights)\n",
-        "print(\"\\nZero-point quantized weights:\")\n",
-        "print(weights_zp_quant)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "5i2N7HC9Mmn7"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "from copy import deepcopy\n",
-        "\n",
-        "# Store original weights\n",
-        "weights = [param.data.clone() for param in model.parameters()]\n",
-        "\n",
-        "# Create model to quantize\n",
-        "model_abs = deepcopy(model)\n",
-        "\n",
-        "# Quantize all model weights\n",
-        "weights_abs = []\n",
-        "for param in model_abs.parameters():\n",
-        "    _, dequantized = absmax_quantize(param.data)\n",
-        "    param.data = dequantized\n",
-        "    weights_abs.append(dequantized)\n",
-        "\n",
-        "# Create model to quantize\n",
-        "model_zp = deepcopy(model)\n",
-        "\n",
-        "# Quantize all model weights\n",
-        "weights_zp = []\n",
-        "for param in model_zp.parameters():\n",
-        "    _, dequantized = zeropoint_quantize(param.data)\n",
-        "    param.data = dequantized\n",
-        "    weights_zp.append(dequantized)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "FlM_jWwpHh34",
-        "outputId": "0705932d-ec5a-4cb1-cc92-08072c014ee7"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 3000x3000 with 2 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import matplotlib.ticker as ticker\n",
-        "\n",
-        "# Flatten weight tensors\n",
-        "weights = np.concatenate([t.cpu().numpy().flatten() for t in weights])\n",
-        "weights_abs = np.concatenate([t.cpu().numpy().flatten() for t in weights_abs])\n",
-        "weights_zp = np.concatenate([t.cpu().numpy().flatten() for t in weights_zp])\n",
-        "\n",
-        "# Set background style\n",
-        "plt.style.use('ggplot')\n",
-        "\n",
-        "# Create figure and axes\n",
-        "fig, axs = plt.subplots(2, figsize=(10,10), dpi=300, sharex=True)\n",
-        "\n",
-        "# Plot the histograms for original and zero-point weights\n",
-        "axs[0].hist(weights, bins=150, alpha=0.5, label='Original weights', color='blue', range=(-2, 2))\n",
-        "axs[0].hist(weights_abs, bins=150, alpha=0.5, label='Absmax weights', color='red', range=(-2, 2))\n",
-        "\n",
-        "# Plot the histograms for original and absmax weights\n",
-        "axs[1].hist(weights, bins=150, alpha=0.5, label='Original weights', color='blue', range=(-2, 2))\n",
-        "axs[1].hist(weights_zp, bins=150, alpha=0.5, label='Zero-point weights', color='green', range=(-2, 2))\n",
-        "\n",
-        "# Add grid\n",
-        "for ax in axs:\n",
-        "    ax.grid(True, linestyle='--', alpha=0.6)\n",
-        "\n",
-        "# Add legend\n",
-        "axs[0].legend()\n",
-        "axs[1].legend()\n",
-        "\n",
-        "# Add title and labels\n",
-        "axs[0].set_title('Comparison of Original and Absmax Quantized Weights', fontsize=16)\n",
-        "axs[1].set_title('Comparison of Original and Zeropoint Quantized Weights', fontsize=16)\n",
-        "\n",
-        "for ax in axs:\n",
-        "    ax.set_xlabel('Weights', fontsize=14)\n",
-        "    ax.set_ylabel('Count', fontsize=14)\n",
-        "    ax.yaxis.set_major_formatter(ticker.EngFormatter()) # Make y-ticks more human readable\n",
-        "\n",
-        "# Improve font\n",
-        "plt.rc('font', size=12)\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "PQS5PyPW_L8v",
-        "outputId": "1b4ee2b5-4097-421e-9e96-f9ae83221137"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Original model:\n",
-            "I have a dream, and it is a dream I believe I would get to live in my future. I love my mother, and there was that one time I had been told that my family wasn't even that strong. And then I got the\n",
-            "--------------------------------------------------\n",
-            "Absmax model:\n",
-            "I have a dream to find out the origin of her hair. She loves it. But there's no way you could be honest about how her hair is made. She must be crazy.\n",
-            "\n",
-            "We found a photo of the hairstyle posted on\n",
-            "--------------------------------------------------\n",
-            "Zeropoint model:\n",
-            "I have a dream of creating two full-time jobs in America—one for people with mental health issues, and one for people who do not suffer from mental illness—or at least have an employment and family history of substance abuse, to work part\n"
-          ]
-        }
-      ],
-      "source": [
-        "def generate_text(model, input_text, max_length=50):\n",
-        "    input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)\n",
-        "    output = model.generate(inputs=input_ids,\n",
-        "                            max_length=max_length,\n",
-        "                            do_sample=True,\n",
-        "                            top_k=30,\n",
-        "                            pad_token_id=tokenizer.eos_token_id,\n",
-        "                            attention_mask=input_ids.new_ones(input_ids.shape))\n",
-        "    return tokenizer.decode(output[0], skip_special_tokens=True)\n",
-        "\n",
-        "# Generate text with original and quantized models\n",
-        "original_text = generate_text(model, \"I have a dream\")\n",
-        "absmax_text   = generate_text(model_abs, \"I have a dream\")\n",
-        "zp_text       = generate_text(model_zp, \"I have a dream\")\n",
-        "\n",
-        "print(f\"Original model:\\n{original_text}\")\n",
-        "print(\"-\" * 50)\n",
-        "print(f\"Absmax model:\\n{absmax_text}\")\n",
-        "print(\"-\" * 50)\n",
-        "print(f\"Zeropoint model:\\n{zp_text}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "py3nuq-WOhwn",
-        "outputId": "dc738c29-170e-4b6e-e3e0-2fe0c7c34e70"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Original perplexity: 15.53\n",
-            "Absmax perplexity:   17.92\n",
-            "Zeropoint perplexity: 17.97\n"
-          ]
-        }
-      ],
-      "source": [
-        "def calculate_perplexity(model, text):\n",
-        "    # Encode the text\n",
-        "    encodings = tokenizer(text, return_tensors='pt').to(device)\n",
-        "\n",
-        "    # Define input_ids and target_ids\n",
-        "    input_ids = encodings.input_ids\n",
-        "    target_ids = input_ids.clone()\n",
-        "\n",
-        "    with torch.no_grad():\n",
-        "        outputs = model(input_ids, labels=target_ids)\n",
-        "\n",
-        "    # Loss calculation\n",
-        "    neg_log_likelihood = outputs.loss\n",
-        "\n",
-        "    # Perplexity calculation\n",
-        "    ppl = torch.exp(neg_log_likelihood)\n",
-        "\n",
-        "    return ppl\n",
-        "\n",
-        "ppl     = calculate_perplexity(model, original_text)\n",
-        "ppl_abs = calculate_perplexity(model_abs, absmax_text)\n",
-        "ppl_zp  = calculate_perplexity(model_zp, absmax_text)\n",
-        "\n",
-        "print(f\"Original perplexity: {ppl.item():.2f}\")\n",
-        "print(f\"Absmax perplexity:   {ppl_abs.item():.2f}\")\n",
-        "print(f\"Zeropoint perplexity: {ppl_zp.item():.2f}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "F_4gVonK0XDE",
-        "outputId": "df61f21e-1d3c-4ba9-9e96-4226f2532435"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Some weights of GPT2LMHeadModel were not initialized from the model checkpoint at gpt2 and are newly initialized: ['lm_head.weight']\n",
-            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Model size: 176,527,896 bytes\n"
-          ]
-        }
-      ],
-      "source": [
-        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-        "\n",
-        "model_int8 = AutoModelForCausalLM.from_pretrained(model_id,\n",
-        "                                             device_map='auto',\n",
-        "                                             load_in_8bit=True,\n",
-        "                                             )\n",
-        "print(f\"Model size: {model_int8.get_memory_footprint():,} bytes\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 964
-        },
-        "id": "XpghDRDlY_6f",
-        "outputId": "eda8f6c8-c53a-4d94-a447-4ec35ed0a8bf"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 3000x1500 with 1 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import matplotlib.ticker as ticker\n",
-        "\n",
-        "# Flatten weight tensors\n",
-        "weights_int8 = [param.data.clone() for param in model_int8.parameters()]\n",
-        "weights_int8 = np.concatenate([t.cpu().numpy().flatten() for t in weights_int8])\n",
-        "\n",
-        "# Set background style\n",
-        "plt.style.use('ggplot')\n",
-        "\n",
-        "# Create figure and axis\n",
-        "fig, ax = plt.subplots(figsize=(10,5), dpi=300)\n",
-        "\n",
-        "# Plot the histograms\n",
-        "ax.hist(weights, bins=150, alpha=0.5, label='Original weights',\n",
-        "        color='blue', range=(-2, 2))\n",
-        "ax.hist(weights_int8, bins=150, alpha=0.5, label='LLM.int8() weights',\n",
-        "        color='red', range=(-2, 2))\n",
-        "\n",
-        "# Add grid\n",
-        "ax.grid(True, linestyle='--', alpha=0.6)\n",
-        "\n",
-        "# Add legend\n",
-        "ax.legend()\n",
-        "\n",
-        "# Add title and labels\n",
-        "ax.set_title('Comparison of Original and Dequantized Weights', fontsize=16)\n",
-        "ax.set_xlabel('Weights', fontsize=14)\n",
-        "ax.set_ylabel('Count', fontsize=14)\n",
-        "plt.gca().yaxis.set_major_formatter(ticker.EngFormatter())\n",
-        "\n",
-        "# Improve font\n",
-        "plt.rc('font', size=12)\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OoDwrdDMZPuR",
-        "outputId": "7db34a58-b2cd-423e-fa83-ce5e531934e2"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Original model:\n",
-            "I have a dream, and it is a dream I believe I would get to live in my future. I love my mother, and there was that one time I had been told that my family wasn't even that strong. And then I got the\n",
-            "--------------------------------------------------\n",
-            "LLM.int8() model:\n",
-            "I have a dream. I don't know what will come of it, but I am going to have to look for something that will be right. I haven't thought about it for a long time, but I have to try to get that thing\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Generate text with quantized model\n",
-        "text_int8 = generate_text(model_int8, \"I have a dream\")\n",
-        "\n",
-        "print(f\"Original model:\\n{original_text}\")\n",
-        "print(\"-\" * 50)\n",
-        "print(f\"LLM.int8() model:\\n{text_int8}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "4XGQ0N9_KyoA",
-        "outputId": "d772d77e-50a5-4b28-90b1-0b407962cdae"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Perplexity (original):   15.53\n",
-            "Perplexity (LLM.int8()): 7.93\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(f\"Perplexity (original):   {ppl.item():.2f}\")\n",
-        "\n",
-        "ppl = calculate_perplexity(model_int8, text_int8)\n",
-        "print(f\"Perplexity (LLM.int8()): {ppl.item():.2f}\")"
-      ]
-    }
-  ],
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-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": [],
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-      "display_name": "Python 3",
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-      "cell_type": "markdown",
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-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Mergekit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
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-      "source": [
-        "# Merge Large Language Models with mergekit\n",
-        "> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
-        "\n",
-        "Model merging only requires a lot of RAM. With a free Google Colab account, you should be able to run it using a T4 GPU (VRAM offloading).\n",
-        "\n",
-        "Examples of merge configurations:\n",
-        "\n",
-        "### TIES-Merging\n",
-        "\n",
-        "```yaml\n",
-        "models:\n",
-        "  - model: mistralai/Mistral-7B-v0.1\n",
-        "    # no parameters necessary for base model\n",
-        "  - model: OpenPipe/mistral-ft-optimized-1218\n",
-        "    parameters:\n",
-        "      density: 0.5\n",
-        "      weight: 0.5\n",
-        "  - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
-        "    parameters:\n",
-        "      density: 0.5\n",
-        "      weight: 0.3\n",
-        "merge_method: ties\n",
-        "base_model: mistralai/Mistral-7B-v0.1\n",
-        "parameters:\n",
-        "  normalize: true\n",
-        "dtype: float16\n",
-        "```\n",
-        "\n",
-        "You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-7B-ties](https://huggingface.co/mlabonne/NeuralPipe-7B-ties).\n",
-        "\n",
-        "### SLERP\n",
-        "\n",
-        "```yaml\n",
-        "slices:\n",
-        "  - sources:\n",
-        "      - model: OpenPipe/mistral-ft-optimized-1218\n",
-        "        layer_range: [0, 32]\n",
-        "      - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
-        "        layer_range: [0, 32]\n",
-        "merge_method: slerp\n",
-        "base_model: OpenPipe/mistral-ft-optimized-1218\n",
-        "parameters:\n",
-        "  t:\n",
-        "    - filter: self_attn\n",
-        "      value: [0, 0.5, 0.3, 0.7, 1]\n",
-        "    - filter: mlp\n",
-        "      value: [1, 0.5, 0.7, 0.3, 0]\n",
-        "    - value: 0.5\n",
-        "dtype: bfloat16\n",
-        "```\n",
-        "\n",
-        "You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-7B-slerp](https://huggingface.co/mlabonne/NeuralPipe-7B-slerp).\n",
-        "\n",
-        "### Passthrough\n",
-        "\n",
-        "```yaml\n",
-        "slices:\n",
-        "  - sources:\n",
-        "    - model: OpenPipe/mistral-ft-optimized-1218\n",
-        "      layer_range: [0, 32]\n",
-        "  - sources:\n",
-        "    - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
-        "      layer_range: [24, 32]\n",
-        "merge_method: passthrough\n",
-        "dtype: bfloat16\n",
-        "```\n",
-        "\n",
-        "You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-9B-merged](https://huggingface.co/mlabonne/NeuralPipe-9B-merged)."
-      ],
-      "metadata": {
-        "id": "o12O0YjJvvLW"
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-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "NPNPie5Eo3EZ"
-      },
-      "outputs": [],
-      "source": [
-        "!git clone https://github.com/cg123/mergekit.git\n",
-        "!cd mergekit && pip install -q -e ."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import yaml\n",
-        "\n",
-        "MODEL_NAME = \"Marcoro14-7B-slerp\"\n",
-        "yaml_config = \"\"\"\n",
-        "slices:\n",
-        "  - sources:\n",
-        "      - model: AIDC-ai-business/Marcoroni-7B-v3\n",
-        "        layer_range: [0, 32]\n",
-        "      - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1\n",
-        "        layer_range: [0, 32]\n",
-        "merge_method: slerp\n",
-        "base_model: AIDC-ai-business/Marcoroni-7B-v3\n",
-        "parameters:\n",
-        "  t:\n",
-        "    - filter: self_attn\n",
-        "      value: [0, 0.5, 0.3, 0.7, 1]\n",
-        "    - filter: mlp\n",
-        "      value: [1, 0.5, 0.7, 0.3, 0]\n",
-        "    - value: 0.5\n",
-        "dtype: bfloat16\n",
-        "\n",
-        "\"\"\"\n",
-        "\n",
-        "# Save config as yaml file\n",
-        "with open('config.yaml', 'w', encoding=\"utf-8\") as f:\n",
-        "    f.write(yaml_config)"
-      ],
-      "metadata": {
-        "id": "LGd7jlfCpNcg"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Merge models\n",
-        "!mergekit-yaml config.yaml merge --copy-tokenizer --allow-crimes --out-shard-size 1B --lazy-unpickle"
-      ],
-      "metadata": {
-        "id": "d5mYzDo1q96y"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip install -qU huggingface_hub\n",
-        "\n",
-        "from huggingface_hub import ModelCard, ModelCardData\n",
-        "from jinja2 import Template\n",
-        "\n",
-        "username = \"mlabonne\"\n",
-        "\n",
-        "template_text = \"\"\"\n",
-        "---\n",
-        "license: apache-2.0\n",
-        "tags:\n",
-        "- merge\n",
-        "- mergekit\n",
-        "- lazymergekit\n",
-        "{%- for model in models %}\n",
-        "- {{ model }}\n",
-        "{%- endfor %}\n",
-        "---\n",
-        "\n",
-        "# {{ model_name }}\n",
-        "\n",
-        "{{ model_name }} is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):\n",
-        "\n",
-        "{%- for model in models %}\n",
-        "* [{{ model }}](https://huggingface.co/{{ model }})\n",
-        "{%- endfor %}\n",
-        "\n",
-        "## 🧩 Configuration\n",
-        "\n",
-        "```yaml\n",
-        "{{- yaml_config -}}\n",
-        "```\n",
-        "\"\"\"\n",
-        "\n",
-        "# Create a Jinja template object\n",
-        "jinja_template = Template(template_text.strip())\n",
-        "\n",
-        "# Get list of models from config\n",
-        "data = yaml.safe_load(yaml_config)\n",
-        "if \"models\" in data:\n",
-        "    models = [data[\"models\"][i][\"model\"] for i in range(len(data[\"models\"])) if \"parameters\" in data[\"models\"][i]]\n",
-        "elif \"parameters\" in data:\n",
-        "    models = [data[\"slices\"][0][\"sources\"][i][\"model\"] for i in range(len(data[\"slices\"][0][\"sources\"]))]\n",
-        "elif \"slices\" in data:\n",
-        "    models = [data[\"slices\"][i][\"sources\"][0][\"model\"] for i in range(len(data[\"slices\"]))]\n",
-        "else:\n",
-        "    raise Exception(\"No models or slices found in yaml config\")\n",
-        "\n",
-        "# Fill the template\n",
-        "content = jinja_template.render(\n",
-        "    model_name=MODEL_NAME,\n",
-        "    models=models,\n",
-        "    yaml_config=yaml_config,\n",
-        "    username=username,\n",
-        ")\n",
-        "\n",
-        "# Save the model card\n",
-        "card = ModelCard(content)\n",
-        "card.save('merge/README.md')"
-      ],
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-        "colab": {
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-          "name": "stdout",
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-            "\u001b[?25h"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from google.colab import userdata\n",
-        "from huggingface_hub import HfApi\n",
-        "\n",
-        "username = \"mlabonne\"\n",
-        "\n",
-        "# Defined in the secrets tab in Google Colab\n",
-        "api = HfApi(token=userdata.get(\"HF_TOKEN\"))\n",
-        "\n",
-        "api.create_repo(\n",
-        "    repo_id=f\"{username}/{MODEL_NAME}\",\n",
-        "    repo_type=\"model\"\n",
-        ")\n",
-        "api.upload_folder(\n",
-        "    repo_id=f\"{username}/{MODEL_NAME}\",\n",
-        "    folder_path=\"merge\",\n",
-        ")"
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-        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Quantize_Llama_2_models_using_GGUF_and_llama_cpp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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-        "# Quantize Llama 2 models using GGUF and llama.cpp\n",
-        "> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
-        "\n",
-        "## Usage\n",
-        "\n",
-        "* `MODEL_ID`: The ID of the model to quantize (e.g., `mlabonne/EvolCodeLlama-7b`).\n",
-        "* `QUANTIZATION_METHOD`: The quantization method to use.\n",
-        "\n",
-        "## Quantization methods\n",
-        "\n",
-        "The names of the quantization methods follow the naming convention: \"q\" + the number of bits + the variant used (detailed below). Here is a list of all the possible quant methods and their corresponding use cases, based on model cards made by [TheBloke](https://huggingface.co/TheBloke/):\n",
-        "\n",
-        "* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.\n",
-        "* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\n",
-        "* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\n",
-        "* `q3_k_s`: Uses Q3_K for all tensors\n",
-        "* `q4_0`: Original quant method, 4-bit.\n",
-        "* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.\n",
-        "* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K\n",
-        "* `q4_k_s`: Uses Q4_K for all tensors\n",
-        "* `q5_0`: Higher accuracy, higher resource usage and slower inference.\n",
-        "* `q5_1`: Even higher accuracy, resource usage and slower inference.\n",
-        "* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K\n",
-        "* `q5_k_s`:  Uses Q5_K for all tensors\n",
-        "* `q6_k`: Uses Q8_K for all tensors\n",
-        "* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.\n",
-        "\n",
-        "As a rule of thumb, **I recommend using Q5_K_M** as it preserves most of the model's performance. Alternatively, you can use Q4_K_M if you want to save some memory. In general, K_M versions are better than K_S versions. I cannot recommend Q2_K or Q3_* versions, as they drastically decrease model performance."
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-        "# Variables\n",
-        "MODEL_ID = \"mlabonne/EvolCodeLlama-7b\"\n",
-        "QUANTIZATION_METHODS = [\"q4_k_m\", \"q5_k_m\"]\n",
-        "\n",
-        "# Constants\n",
-        "MODEL_NAME = MODEL_ID.split('/')[-1]\n",
-        "\n",
-        "# Install llama.cpp\n",
-        "!git clone https://github.com/ggerganov/llama.cpp\n",
-        "!cd llama.cpp && git pull && make clean && LLAMA_CUBLAS=1 make\n",
-        "!pip install -r llama.cpp/requirements.txt\n",
-        "\n",
-        "# Download model\n",
-        "!git lfs install\n",
-        "!git clone https://huggingface.co/{MODEL_ID}\n",
-        "\n",
-        "# Convert to fp16\n",
-        "fp16 = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.fp16.bin\"\n",
-        "!python llama.cpp/convert.py {MODEL_NAME} --outtype f16 --outfile {fp16}\n",
-        "\n",
-        "# Quantize the model for each method in the QUANTIZATION_METHODS list\n",
-        "for method in QUANTIZATION_METHODS:\n",
-        "    qtype = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.{method.upper()}.gguf\"\n",
-        "    !./llama.cpp/quantize {fp16} {qtype} {method}"
-      ],
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-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fD24jJxq7t3k",
-        "outputId": "94954934-0829-44e9-a5e5-262c17e162d0"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "ggml_init_cublas: found 1 CUDA devices:\n",
-            "  Device 0: Tesla T4, compute capability 7.5\n",
-            "main: build = 1100 (dd0dc36)\n",
-            "main: quantizing 'EvolCodeLlama-7b/evolcodellama-7b.gguf.fp16.bin' to 'EvolCodeLlama-7b/evolcodellama-7b.gguf.q4_k_s.bin' as Q4_K_S\n",
-            "llama_model_loader: loaded meta data with 16 key-value pairs and 291 tensors from EvolCodeLlama-7b/evolcodellama-7b.gguf.fp16.bin (version GGUF V1 (support until nov 2023))\n",
-            "llama_model_loader: - tensor    0:                token_embd.weight f16      [  4096, 32016,     1,     1 ]\n",
-            "llama_model_loader: - tensor    1:              blk.0.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    2:              blk.0.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    3:              blk.0.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    4:         blk.0.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    5:            blk.0.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor    6:              blk.0.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor    7:            blk.0.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    8:           blk.0.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor    9:            blk.0.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   10:              blk.1.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   11:              blk.1.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   12:              blk.1.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   13:         blk.1.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   14:            blk.1.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   15:              blk.1.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   16:            blk.1.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   17:           blk.1.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   18:            blk.1.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   19:              blk.2.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   20:              blk.2.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   21:              blk.2.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   22:         blk.2.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   23:            blk.2.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   24:              blk.2.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   25:            blk.2.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   26:           blk.2.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   27:            blk.2.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   28:              blk.3.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   29:              blk.3.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   30:              blk.3.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   31:         blk.3.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   32:            blk.3.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   33:              blk.3.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   34:            blk.3.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   35:           blk.3.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   36:            blk.3.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   37:              blk.4.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   38:              blk.4.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   39:              blk.4.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   40:         blk.4.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   41:            blk.4.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   42:              blk.4.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   43:            blk.4.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   44:           blk.4.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   45:            blk.4.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   46:              blk.5.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   47:              blk.5.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   48:              blk.5.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   49:         blk.5.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   50:            blk.5.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   51:              blk.5.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   52:            blk.5.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   53:           blk.5.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   54:            blk.5.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   55:              blk.6.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   56:              blk.6.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   57:              blk.6.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   58:         blk.6.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   59:            blk.6.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   60:              blk.6.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   61:            blk.6.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   62:           blk.6.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   63:            blk.6.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   64:              blk.7.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   65:              blk.7.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   66:              blk.7.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   67:         blk.7.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   68:            blk.7.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   69:              blk.7.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   70:            blk.7.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   71:           blk.7.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   72:            blk.7.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   73:              blk.8.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   74:              blk.8.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   75:              blk.8.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   76:         blk.8.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   77:            blk.8.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   78:              blk.8.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   79:            blk.8.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   80:           blk.8.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   81:            blk.8.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   82:              blk.9.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   83:              blk.9.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   84:              blk.9.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   85:         blk.9.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   86:            blk.9.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   87:              blk.9.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   88:            blk.9.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   89:           blk.9.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   90:            blk.9.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   91:             blk.10.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   92:             blk.10.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   93:             blk.10.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   94:        blk.10.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   95:           blk.10.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   96:             blk.10.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   97:           blk.10.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   98:          blk.10.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   99:           blk.10.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  100:             blk.11.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  101:             blk.11.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  102:             blk.11.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  103:        blk.11.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  104:           blk.11.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  105:             blk.11.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  106:           blk.11.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  107:          blk.11.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  108:           blk.11.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  109:             blk.12.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  110:             blk.12.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  111:             blk.12.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  112:        blk.12.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  113:           blk.12.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  114:             blk.12.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  115:           blk.12.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  116:          blk.12.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  117:           blk.12.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  118:             blk.13.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  119:             blk.13.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  120:             blk.13.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  121:        blk.13.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  122:           blk.13.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  123:             blk.13.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  124:           blk.13.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  125:          blk.13.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  126:           blk.13.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  127:             blk.14.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  128:             blk.14.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  129:             blk.14.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  130:        blk.14.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  131:           blk.14.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  132:             blk.14.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  133:           blk.14.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  134:          blk.14.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  135:           blk.14.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  136:             blk.15.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  137:             blk.15.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  138:             blk.15.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  139:        blk.15.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  140:           blk.15.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  141:             blk.15.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  142:           blk.15.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  143:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  144:           blk.15.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  145:             blk.16.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  146:             blk.16.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  147:             blk.16.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  148:        blk.16.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  149:           blk.16.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  150:             blk.16.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  151:           blk.16.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  152:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  153:           blk.16.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  154:             blk.17.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  155:             blk.17.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  156:             blk.17.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  157:        blk.17.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  158:           blk.17.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  159:             blk.17.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  160:           blk.17.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  161:          blk.17.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  162:           blk.17.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  163:             blk.18.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  164:             blk.18.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  165:             blk.18.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  166:        blk.18.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  167:           blk.18.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  168:             blk.18.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  169:           blk.18.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  170:          blk.18.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  171:           blk.18.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  172:             blk.19.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  173:             blk.19.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  174:             blk.19.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  175:        blk.19.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  176:           blk.19.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  177:             blk.19.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  178:           blk.19.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  179:          blk.19.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  180:           blk.19.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  181:             blk.20.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  182:             blk.20.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  183:             blk.20.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  184:        blk.20.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  185:           blk.20.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  186:             blk.20.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  187:           blk.20.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  188:          blk.20.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  189:           blk.20.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  190:             blk.21.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  191:             blk.21.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  192:             blk.21.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  193:        blk.21.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  194:           blk.21.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  195:             blk.21.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  196:           blk.21.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  197:          blk.21.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  198:           blk.21.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  199:             blk.22.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  200:             blk.22.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  201:             blk.22.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  202:        blk.22.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  203:           blk.22.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  204:             blk.22.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  205:           blk.22.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  206:          blk.22.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  207:           blk.22.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  208:             blk.23.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  209:             blk.23.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  210:             blk.23.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  211:        blk.23.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  212:           blk.23.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  213:             blk.23.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  214:           blk.23.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  215:          blk.23.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  216:           blk.23.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  217:             blk.24.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  218:             blk.24.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  219:             blk.24.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  220:        blk.24.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  221:           blk.24.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  222:             blk.24.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  223:           blk.24.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  224:          blk.24.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  225:           blk.24.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  226:             blk.25.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  227:             blk.25.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  228:             blk.25.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  229:        blk.25.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  230:           blk.25.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  231:             blk.25.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  232:           blk.25.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  233:          blk.25.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  234:           blk.25.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  235:             blk.26.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  236:             blk.26.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  237:             blk.26.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  238:        blk.26.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  239:           blk.26.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  240:             blk.26.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  241:           blk.26.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  242:          blk.26.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  243:           blk.26.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  244:             blk.27.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  245:             blk.27.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  246:             blk.27.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  247:        blk.27.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  248:           blk.27.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  249:             blk.27.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  250:           blk.27.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  251:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  252:           blk.27.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  253:             blk.28.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  254:             blk.28.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  255:             blk.28.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  256:        blk.28.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  257:           blk.28.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  258:             blk.28.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  259:           blk.28.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  260:          blk.28.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  261:           blk.28.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  262:             blk.29.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  263:             blk.29.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  264:             blk.29.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  265:        blk.29.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  266:           blk.29.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  267:             blk.29.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  268:           blk.29.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  269:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  270:           blk.29.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  271:             blk.30.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  272:             blk.30.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  273:             blk.30.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  274:        blk.30.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  275:           blk.30.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  276:             blk.30.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  277:           blk.30.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  278:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  279:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  280:             blk.31.attn_q.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  281:             blk.31.attn_k.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  282:             blk.31.attn_v.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  283:        blk.31.attn_output.weight f16      [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  284:           blk.31.ffn_gate.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  285:             blk.31.ffn_up.weight f16      [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  286:           blk.31.ffn_down.weight f16      [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  287:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  288:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  289:               output_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  290:                    output.weight f16      [  4096, 32016,     1,     1 ]\n",
-            "llama_model_loader: - kv   0:                       general.architecture str     \n",
-            "llama_model_loader: - kv   1:                               general.name str     \n",
-            "llama_model_loader: - kv   2:                       llama.context_length u32     \n",
-            "llama_model_loader: - kv   3:                     llama.embedding_length u32     \n",
-            "llama_model_loader: - kv   4:                          llama.block_count u32     \n",
-            "llama_model_loader: - kv   5:                  llama.feed_forward_length u32     \n",
-            "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32     \n",
-            "llama_model_loader: - kv   7:                 llama.attention.head_count u32     \n",
-            "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32     \n",
-            "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32     \n",
-            "llama_model_loader: - kv  10:                       llama.rope.freq_base f32     \n",
-            "llama_model_loader: - kv  11:                          general.file_type u32     \n",
-            "llama_model_loader: - kv  12:                       tokenizer.ggml.model str     \n",
-            "llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr     \n",
-            "llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr     \n",
-            "llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr     \n",
-            "llama_model_loader: - type  f32:   65 tensors\n",
-            "llama_model_loader: - type  f16:  226 tensors\n",
-            "llama_model_quantize_internal: meta size = 741408 bytes\n",
-            "[   1/ 291]                    token_embd.weight - [ 4096, 32016,     1,     1], type =    f16, quantizing to q4_K .. size =   250.12 MB ->    70.35 MB | hist: \n",
-            "[   2/ 291]                  blk.0.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[   3/ 291]                  blk.0.attn_k.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[   4/ 291]                  blk.0.attn_v.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MB ->    11.00 MB | hist: \n",
-            "[   5/ 291]             blk.0.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[   6/ 291]                blk.0.ffn_gate.weight - [ 4096, 11008,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[   7/ 291]                  blk.0.ffn_up.weight - [ 4096, 11008,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[   8/ 291]                blk.0.ffn_down.weight - [11008,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    86.00 MB ->    29.56 MB | hist: \n",
-            "[   9/ 291]               blk.0.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[  10/ 291]                blk.0.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[  11/ 291]                  blk.1.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[  12/ 291]                  blk.1.attn_k.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[  13/ 291]                  blk.1.attn_v.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MB ->    11.00 MB | hist: \n",
-            "[  14/ 291]             blk.1.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[  15/ 291]                blk.1.ffn_gate.weight - [ 4096, 11008,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[  16/ 291]                  blk.1.ffn_up.weight - [ 4096, 11008,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[  17/ 291]                blk.1.ffn_down.weight - [11008,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    86.00 MB ->    29.56 MB | hist: \n",
-            "[  18/ 291]               blk.1.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[  19/ 291]                blk.1.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[  20/ 291]                  blk.2.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[  21/ 291]                  blk.2.attn_k.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
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-            "[ 271/ 291]               blk.29.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[ 272/ 291]                 blk.30.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[ 273/ 291]                 blk.30.attn_k.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[ 274/ 291]                 blk.30.attn_v.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[ 275/ 291]            blk.30.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[ 276/ 291]               blk.30.ffn_gate.weight - [ 4096, 11008,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[ 277/ 291]                 blk.30.ffn_up.weight - [ 4096, 11008,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[ 278/ 291]               blk.30.ffn_down.weight - [11008,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[ 279/ 291]              blk.30.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[ 280/ 291]               blk.30.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[ 281/ 291]                 blk.31.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[ 282/ 291]                 blk.31.attn_k.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[ 283/ 291]                 blk.31.attn_v.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[ 284/ 291]            blk.31.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    32.00 MB ->     9.00 MB | hist: \n",
-            "[ 285/ 291]               blk.31.ffn_gate.weight - [ 4096, 11008,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[ 286/ 291]                 blk.31.ffn_up.weight - [ 4096, 11008,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[ 287/ 291]               blk.31.ffn_down.weight - [11008,  4096,     1,     1], type =    f16, quantizing to q4_K .. size =    86.00 MB ->    24.19 MB | hist: \n",
-            "[ 288/ 291]              blk.31.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[ 289/ 291]               blk.31.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[ 290/ 291]                   output_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
-            "[ 291/ 291]                        output.weight - [ 4096, 32016,     1,     1], type =    f16, quantizing to q6_K .. size =   250.12 MB ->   102.59 MB | hist: \n",
-            "llama_model_quantize_internal: model size  = 12853.27 MB\n",
-            "llama_model_quantize_internal: quant size  =  3677.45 MB\n",
-            "\n",
-            "main: quantize time = 1089230.46 ms\n",
-            "main:    total time = 1089230.46 ms\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Run inference\n",
-        "\n",
-        "Here is a simple script to run your quantized models. I'm offloading every layer to the GPU (35 for a 7b parameter model) to speed up inference."
-      ],
-      "metadata": {
-        "id": "WqI1CPiXI4dP"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import os\n",
-        "\n",
-        "model_list = [file for file in os.listdir(MODEL_NAME) if \"gguf\" in file]\n",
-        "\n",
-        "prompt = input(\"Enter your prompt: \")\n",
-        "chosen_method = input(\"Name of the model (options: \" + \", \".join(model_list) + \"): \")\n",
-        "\n",
-        "# Verify the chosen method is in the list\n",
-        "if chosen_method not in model_list:\n",
-        "    print(\"Invalid name\")\n",
-        "else:\n",
-        "    qtype = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.{method.upper()}.gguf\"\n",
-        "    !./llama.cpp/main -m {qtype} -n 128 --color -ngl 35 -p \"{prompt}\""
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "vNPL9WYg78l-",
-        "outputId": "3c3e7d2f-f0de-429d-fd97-dab480bc514a"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Enter your prompt: prompt\n",
-            "Please specify the quantization method to run the model (options: q4_k_s): q4_k_s\n",
-            "main: build = 1100 (dd0dc36)\n",
-            "main: seed  = 1693227123\n",
-            "ggml_init_cublas: found 1 CUDA devices:\n",
-            "  Device 0: Tesla T4, compute capability 7.5\n",
-            "llama_model_loader: loaded meta data with 17 key-value pairs and 291 tensors from EvolCodeLlama-7b/evolcodellama-7b.gguf.q4_k_s.bin (version GGUF V2 (latest))\n",
-            "llama_model_loader: - tensor    0:                token_embd.weight q4_K     [  4096, 32016,     1,     1 ]\n",
-            "llama_model_loader: - tensor    1:              blk.0.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    2:              blk.0.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    3:              blk.0.attn_v.weight q5_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    4:         blk.0.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    5:            blk.0.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor    6:              blk.0.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor    7:            blk.0.ffn_down.weight q5_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor    8:           blk.0.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor    9:            blk.0.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   10:              blk.1.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   11:              blk.1.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   12:              blk.1.attn_v.weight q5_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   13:         blk.1.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   14:            blk.1.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   15:              blk.1.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   16:            blk.1.ffn_down.weight q5_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   17:           blk.1.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   18:            blk.1.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   19:              blk.2.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   20:              blk.2.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   21:              blk.2.attn_v.weight q5_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   22:         blk.2.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   23:            blk.2.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   24:              blk.2.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   25:            blk.2.ffn_down.weight q5_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   26:           blk.2.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   27:            blk.2.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   28:              blk.3.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   29:              blk.3.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   30:              blk.3.attn_v.weight q5_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   31:         blk.3.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   32:            blk.3.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   33:              blk.3.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   34:            blk.3.ffn_down.weight q5_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   35:           blk.3.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   36:            blk.3.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   37:              blk.4.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   38:              blk.4.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   39:              blk.4.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   40:         blk.4.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   41:            blk.4.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   42:              blk.4.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   43:            blk.4.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   44:           blk.4.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   45:            blk.4.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   46:              blk.5.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   47:              blk.5.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   48:              blk.5.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   49:         blk.5.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   50:            blk.5.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   51:              blk.5.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   52:            blk.5.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   53:           blk.5.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   54:            blk.5.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   55:              blk.6.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   56:              blk.6.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   57:              blk.6.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   58:         blk.6.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   59:            blk.6.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   60:              blk.6.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   61:            blk.6.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   62:           blk.6.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   63:            blk.6.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   64:              blk.7.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   65:              blk.7.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   66:              blk.7.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   67:         blk.7.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   68:            blk.7.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   69:              blk.7.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   70:            blk.7.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   71:           blk.7.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   72:            blk.7.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   73:              blk.8.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   74:              blk.8.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   75:              blk.8.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   76:         blk.8.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   77:            blk.8.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   78:              blk.8.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   79:            blk.8.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   80:           blk.8.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   81:            blk.8.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   82:              blk.9.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   83:              blk.9.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   84:              blk.9.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   85:         blk.9.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   86:            blk.9.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   87:              blk.9.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   88:            blk.9.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   89:           blk.9.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   90:            blk.9.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   91:             blk.10.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   92:             blk.10.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   93:             blk.10.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   94:        blk.10.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   95:           blk.10.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   96:             blk.10.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor   97:           blk.10.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor   98:          blk.10.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor   99:           blk.10.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  100:             blk.11.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  101:             blk.11.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  102:             blk.11.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  103:        blk.11.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  104:           blk.11.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  105:             blk.11.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  106:           blk.11.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  107:          blk.11.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  108:           blk.11.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  109:             blk.12.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  110:             blk.12.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  111:             blk.12.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  112:        blk.12.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  113:           blk.12.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  114:             blk.12.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  115:           blk.12.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  116:          blk.12.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  117:           blk.12.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  118:             blk.13.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  119:             blk.13.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  120:             blk.13.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  121:        blk.13.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  122:           blk.13.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  123:             blk.13.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  124:           blk.13.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  125:          blk.13.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  126:           blk.13.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  127:             blk.14.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  128:             blk.14.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  129:             blk.14.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  130:        blk.14.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  131:           blk.14.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  132:             blk.14.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  133:           blk.14.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  134:          blk.14.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  135:           blk.14.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  136:             blk.15.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  137:             blk.15.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  138:             blk.15.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  139:        blk.15.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  140:           blk.15.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  141:             blk.15.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  142:           blk.15.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  143:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  144:           blk.15.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  145:             blk.16.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  146:             blk.16.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  147:             blk.16.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  148:        blk.16.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  149:           blk.16.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  150:             blk.16.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  151:           blk.16.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  152:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  153:           blk.16.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  154:             blk.17.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  155:             blk.17.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  156:             blk.17.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  157:        blk.17.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  158:           blk.17.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  159:             blk.17.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  160:           blk.17.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  161:          blk.17.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  162:           blk.17.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  163:             blk.18.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  164:             blk.18.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  165:             blk.18.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  166:        blk.18.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  167:           blk.18.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  168:             blk.18.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  169:           blk.18.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  170:          blk.18.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  171:           blk.18.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  172:             blk.19.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  173:             blk.19.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  174:             blk.19.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  175:        blk.19.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  176:           blk.19.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  177:             blk.19.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  178:           blk.19.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  179:          blk.19.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  180:           blk.19.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  181:             blk.20.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  182:             blk.20.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  183:             blk.20.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  184:        blk.20.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  185:           blk.20.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  186:             blk.20.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  187:           blk.20.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  188:          blk.20.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  189:           blk.20.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  190:             blk.21.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  191:             blk.21.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  192:             blk.21.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  193:        blk.21.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  194:           blk.21.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  195:             blk.21.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  196:           blk.21.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  197:          blk.21.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  198:           blk.21.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  199:             blk.22.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  200:             blk.22.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  201:             blk.22.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  202:        blk.22.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  203:           blk.22.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  204:             blk.22.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  205:           blk.22.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  206:          blk.22.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  207:           blk.22.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  208:             blk.23.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  209:             blk.23.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  210:             blk.23.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  211:        blk.23.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  212:           blk.23.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  213:             blk.23.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  214:           blk.23.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  215:          blk.23.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  216:           blk.23.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  217:             blk.24.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  218:             blk.24.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  219:             blk.24.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  220:        blk.24.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  221:           blk.24.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  222:             blk.24.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  223:           blk.24.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  224:          blk.24.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  225:           blk.24.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  226:             blk.25.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  227:             blk.25.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  228:             blk.25.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  229:        blk.25.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  230:           blk.25.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  231:             blk.25.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  232:           blk.25.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  233:          blk.25.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  234:           blk.25.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  235:             blk.26.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  236:             blk.26.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  237:             blk.26.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  238:        blk.26.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  239:           blk.26.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  240:             blk.26.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  241:           blk.26.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  242:          blk.26.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  243:           blk.26.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  244:             blk.27.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  245:             blk.27.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  246:             blk.27.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  247:        blk.27.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  248:           blk.27.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  249:             blk.27.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  250:           blk.27.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  251:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  252:           blk.27.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  253:             blk.28.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  254:             blk.28.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  255:             blk.28.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  256:        blk.28.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  257:           blk.28.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  258:             blk.28.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  259:           blk.28.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  260:          blk.28.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  261:           blk.28.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  262:             blk.29.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  263:             blk.29.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  264:             blk.29.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  265:        blk.29.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  266:           blk.29.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  267:             blk.29.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  268:           blk.29.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  269:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  270:           blk.29.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  271:             blk.30.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  272:             blk.30.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  273:             blk.30.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  274:        blk.30.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  275:           blk.30.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  276:             blk.30.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  277:           blk.30.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  278:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  279:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  280:             blk.31.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  281:             blk.31.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  282:             blk.31.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  283:        blk.31.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  284:           blk.31.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  285:             blk.31.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
-            "llama_model_loader: - tensor  286:           blk.31.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
-            "llama_model_loader: - tensor  287:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  288:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  289:               output_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-            "llama_model_loader: - tensor  290:                    output.weight q6_K     [  4096, 32016,     1,     1 ]\n",
-            "llama_model_loader: - kv   0:                       general.architecture str     \n",
-            "llama_model_loader: - kv   1:                               general.name str     \n",
-            "llama_model_loader: - kv   2:                       llama.context_length u32     \n",
-            "llama_model_loader: - kv   3:                     llama.embedding_length u32     \n",
-            "llama_model_loader: - kv   4:                          llama.block_count u32     \n",
-            "llama_model_loader: - kv   5:                  llama.feed_forward_length u32     \n",
-            "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32     \n",
-            "llama_model_loader: - kv   7:                 llama.attention.head_count u32     \n",
-            "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32     \n",
-            "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32     \n",
-            "llama_model_loader: - kv  10:                       llama.rope.freq_base f32     \n",
-            "llama_model_loader: - kv  11:                          general.file_type u32     \n",
-            "llama_model_loader: - kv  12:                       tokenizer.ggml.model str     \n",
-            "llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr     \n",
-            "llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr     \n",
-            "llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr     \n",
-            "llama_model_loader: - kv  16:               general.quantization_version u32     \n",
-            "llama_model_loader: - type  f32:   65 tensors\n",
-            "llama_model_loader: - type q4_K:  217 tensors\n",
-            "llama_model_loader: - type q5_K:    8 tensors\n",
-            "llama_model_loader: - type q6_K:    1 tensors\n",
-            "llm_load_print_meta: format         = GGUF V2 (latest)\n",
-            "llm_load_print_meta: arch           = llama\n",
-            "llm_load_print_meta: vocab type     = SPM\n",
-            "llm_load_print_meta: n_vocab        = 32016\n",
-            "llm_load_print_meta: n_merges       = 0\n",
-            "llm_load_print_meta: n_ctx_train    = 16384\n",
-            "llm_load_print_meta: n_ctx          = 512\n",
-            "llm_load_print_meta: n_embd         = 4096\n",
-            "llm_load_print_meta: n_head         = 32\n",
-            "llm_load_print_meta: n_head_kv      = 32\n",
-            "llm_load_print_meta: n_layer        = 32\n",
-            "llm_load_print_meta: n_rot          = 128\n",
-            "llm_load_print_meta: n_gqa          = 1\n",
-            "llm_load_print_meta: f_norm_eps     = 1.0e-05\n",
-            "llm_load_print_meta: f_norm_rms_eps = 1.0e-05\n",
-            "llm_load_print_meta: n_ff           = 11008\n",
-            "llm_load_print_meta: freq_base      = 1000000.0\n",
-            "llm_load_print_meta: freq_scale     = 1\n",
-            "llm_load_print_meta: model type     = 7B\n",
-            "llm_load_print_meta: model ftype    = mostly Q4_K - Small\n",
-            "llm_load_print_meta: model size     = 6.74 B\n",
-            "llm_load_print_meta: general.name   = LLaMA\n",
-            "llm_load_print_meta: BOS token = 1 '<s>'\n",
-            "llm_load_print_meta: EOS token = 2 '</s>'\n",
-            "llm_load_print_meta: UNK token = 0 '<unk>'\n",
-            "llm_load_print_meta: LF token  = 13 '<0x0A>'\n",
-            "llm_load_tensors: ggml ctx size =    0.09 MB\n",
-            "llm_load_tensors: using CUDA for GPU acceleration\n",
-            "llm_load_tensors: mem required  =   70.44 MB (+  256.00 MB per state)\n",
-            "llm_load_tensors: offloading 32 repeating layers to GPU\n",
-            "llm_load_tensors: offloading non-repeating layers to GPU\n",
-            "llm_load_tensors: offloading v cache to GPU\n",
-            "llm_load_tensors: offloading k cache to GPU\n",
-            "llm_load_tensors: offloaded 35/35 layers to GPU\n",
-            "llm_load_tensors: VRAM used: 3864 MB\n",
-            "..................................................................................................\n",
-            "llama_new_context_with_model: kv self size  =  256.00 MB\n",
-            "llama_new_context_with_model: compute buffer total size =   71.94 MB\n",
-            "llama_new_context_with_model: VRAM scratch buffer: 70.53 MB\n",
-            "\n",
-            "system_info: n_threads = 2 / 2 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | \n",
-            "sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000\n",
-            "generate: n_ctx = 512, n_batch = 512, n_predict = 128, n_keep = 0\n",
-            "\n",
-            "\n",
-            "\u001b[33m prompt\u001b[0m.\t\t\t\t\n",
-            "\t\t\t\t\tif( !this->m_pMiscSettings ) { return; }\t// If no misc settings, do nothing\n",
-            "\t\t\t\t\t\n",
-            "\t\t\t\t\t// Get the value of the checkbox for \"Always on top\"\n",
-            "\t\t\t\t\tbool alwaysOnTop = this->m_pMiscSettings->GetBool(L\"AlwaysOnTop\", false);\n",
-            "\t\t\t\t\tthis->SetWindowPos((alwaysOnTop ? HWND_TOPMOST : HWND_NOTOPMOST\n",
-            "llama_print_timings:        load time =  1392.10 ms\n",
-            "llama_print_timings:      sample time =   147.99 ms /   128 runs   (    1.16 ms per token,   864.92 tokens per second)\n",
-            "llama_print_timings: prompt eval time =   261.80 ms /     2 tokens (  130.90 ms per token,     7.64 tokens per second)\n",
-            "llama_print_timings:        eval time =  5923.18 ms /   127 runs   (   46.64 ms per token,    21.44 tokens per second)\n",
-            "llama_print_timings:       total time =  6370.96 ms\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Push to hub\n",
-        "\n",
-        "To push your model to the hub, you'll need to input your Hugging Face token (https://huggingface.co/settings/tokens) in Google Colab's \"Secrets\" tab. The following code creates a new repo with the \"-GGUF\" suffix. Don't forget to change the `username` variable."
-      ],
-      "metadata": {
-        "id": "Ar8pO7bb80US"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip install -q huggingface_hub\n",
-        "from huggingface_hub import create_repo, HfApi\n",
-        "from google.colab import userdata\n",
-        "\n",
-        "# Defined in the secrets tab in Google Colab\n",
-        "hf_token = userdata.get('huggingface')\n",
-        "\n",
-        "api = HfApi()\n",
-        "username = \"mlabonne\"\n",
-        "\n",
-        "# Create empty repo\n",
-        "create_repo(\n",
-        "    repo_id = f\"{username}/{MODEL_NAME}-GGUF\",\n",
-        "    repo_type=\"model\",\n",
-        "    exist_ok=True,\n",
-        "    token=hf_token\n",
-        ")\n",
-        "\n",
-        "# Upload gguf files\n",
-        "api.upload_folder(\n",
-        "    folder_path=MODEL_NAME,\n",
-        "    repo_id=f\"{username}/{MODEL_NAME}-GGUF\",\n",
-        "    allow_patterns=f\"*.gguf\",\n",
-        "    token=hf_token\n",
-        ")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 163,
-          "referenced_widgets": [
-            "c281b60e104f4c5da547bbdd7208d4bc",
-            "74b084c97f6f46d293a197bf9804460c",
-            "1409574c4f9742e7a711965dd2c8ad87",
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-            "24d3d72f5de54de8a1ded4e528dde332",
-            "e90cb0ce526a4556bc643ba6c5485661",
-            "76e7372656b745c889b9283b76c04148",
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-            "7944af54f2564920822d5d4b348896c4",
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-            "bf612001ad354ea19de6ee45a166a43c",
-            "a8e4691970b14955bfb4865bcef5e912",
-            "2e2fabac70484c1c8b16fa6ca8fd8537",
-            "bf53c635fa374420ad850eea22cd1e31",
-            "065d59126a734c1aa096ba40cd4a129f",
-            "e8855d5678a342f5a33171aa74d3b7bc",
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-            "6ae4640196da492fadafeb63f4bc89d2",
-            "cef83433dbea4f529f43722fe78a8baf",
-            "845ba8115d5140ac9ee22af4a9e6a03b",
-            "cdd888041aca4dcf8adc785309071fc6",
-            "cf63214cb4f8442999fa5b971035fe4f",
-            "7d9b22f2b7fe4a749f989e247bce446a",
-            "7f8e268db8144adfb09d089784d8411a"
-          ]
-        },
-        "id": "UOyKfUD-8jmh",
-        "outputId": "3c8df47b-f350-4251-a19f-4b9fb1116381"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/268.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.7/268.8 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "c281b60e104f4c5da547bbdd7208d4bc"
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-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/Quantize_models_with_ExLlamaV2.ipynb b/Quantize_models_with_ExLlamaV2.ipynb
deleted file mode 100644
index 8ef6d36..0000000
--- a/Quantize_models_with_ExLlamaV2.ipynb
+++ /dev/null
@@ -1,8505 +0,0 @@
-{
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-              "IPY_MODEL_23fc55e87153404d90f55930f7f73988",
-              "IPY_MODEL_903957b2c1404703b2e59a2875da77ec"
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-            "value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>"
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-            "layout": "IPY_MODEL_3efa5c88dc2d491c99678d73e4144eeb",
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-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Quantize_models_with_ExLlamaV2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# ExLlamaV2: The Fastest Library to Run LLMs\n",
-        "\n",
-        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne) as part of the 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)."
-      ],
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-      "execution_count": null,
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-        "id": "4niZ8igkiXZb"
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-      "source": [
-        "# Install ExLLamaV2\n",
-        "!git clone https://github.com/turboderp/exllamav2\n",
-        "!pip install -e exllamav2"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "MODEL_NAME = \"zephyr-7b-beta\"\n",
-        "BPW = 5.0\n",
-        "\n",
-        "# Download model\n",
-        "!git lfs install\n",
-        "!git clone https://huggingface.co/HuggingFaceH4/{MODEL_NAME}\n",
-        "!mv {MODEL_NAME} base_model\n",
-        "!rm base_mode/*.bin\n",
-        "\n",
-        "# Download dataset\n",
-        "!wget https://huggingface.co/datasets/wikitext/resolve/9a9e482b5987f9d25b3a9b2883fc6cc9fd8071b3/wikitext-103-v1/wikitext-test.parquet"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "elHDPmXAil0c",
-        "outputId": "5b732dac-fead-4ab2-f3e8-7b3622d3c690"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Git LFS initialized.\n",
-            "Cloning into 'zephyr-7b-beta'...\n",
-            "remote: Enumerating objects: 55, done.\u001b[K\n",
-            "remote: Total 55 (delta 0), reused 0 (delta 0), pack-reused 55\u001b[K\n",
-            "Unpacking objects: 100% (55/55), 534.67 KiB | 4.73 MiB/s, done.\n",
-            "Filtering content: 100% (10/10), 13.48 GiB | 129.35 MiB/s, done.\n",
-            "rm: cannot remove 'base_mode/*.bin': No such file or directory\n",
-            "--2023-11-03 18:05:02--  https://huggingface.co/datasets/wikitext/resolve/9a9e482b5987f9d25b3a9b2883fc6cc9fd8071b3/wikitext-103-v1/wikitext-test.parquet\n",
-            "Resolving huggingface.co (huggingface.co)... 65.8.178.27, 65.8.178.93, 65.8.178.118, ...\n",
-            "Connecting to huggingface.co (huggingface.co)|65.8.178.27|:443... connected.\n",
-            "HTTP request sent, awaiting response... 200 OK\n",
-            "Length: 721735 (705K)\n",
-            "Saving to: ‘wikitext-test.parquet’\n",
-            "\n",
-            "wikitext-test.parqu 100%[===================>] 704.82K  --.-KB/s    in 0.1s    \n",
-            "\n",
-            "2023-11-03 18:05:02 (5.57 MB/s) - ‘wikitext-test.parquet’ saved [721735/721735]\n",
-            "\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Quantize model\n",
-        "!mkdir quant\n",
-        "!python exllamav2/convert.py \\\n",
-        "    -i base_model \\\n",
-        "    -o quant \\\n",
-        "    -c wikitext-test.parquet \\\n",
-        "    -b {BPW}"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "jigQLg8Fis1f",
-        "outputId": "5f2e97ee-f823-45f7-f39a-f4c6a774b587"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01444\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01401\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01332\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00876\n",
-            " -- Time: 3.78 seconds\n",
-            " -- Linear: model.layers.7.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19280\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17356\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16492\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14247\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09048\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08221\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10930\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09792\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09219\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07561\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07156\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05653\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04767\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04434\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04352\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02850\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02416\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02348\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02138\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02088\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01587\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01675\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01480\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01294\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Layer: model.layers.7 (MLP)\n",
-            " -- Linear: model.layers.7.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.17192\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16163\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15815\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14380\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08135\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07770\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09087\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08363\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08210\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07280\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06958\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04646\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04014\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03912\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03888\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02327\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02036\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02012\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01866\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01851\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01246\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01264\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01212\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00882\n",
-            " -- Time: 16.78 seconds\n",
-            " -- Linear: model.layers.7.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24041\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22653\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.22191\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.20179\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11401\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10911\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12697\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11680\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11501\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10219\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09774\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06494\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05600\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05474\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05444\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03253\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02831\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02799\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02593\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02574\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01738\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01727\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01695\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01176\n",
-            " -- Time: 16.71 seconds\n",
-            " -- Linear: model.layers.7.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.20930\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18827\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17987\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16126\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09629\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.08799\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11395\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10350\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.09877\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08309\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07956\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05813\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04962\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04637\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04557\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02919\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02440\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02402\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02185\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02135\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01579\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01587\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01471\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01128\n",
-            " -- Time: 26.80 seconds\n",
-            " -- Layer: model.layers.8 (Attention)\n",
-            " -- Linear: model.layers.8.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.07321\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.06490\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.05988\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.05294\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03374\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.02962\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.04266\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.03904\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.03463\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.02839\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.02717\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02166\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.01866\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01627\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01567\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01085\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.00853\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00809\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00748\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00708\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00570\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00571\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00489\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00384\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Linear: model.layers.8.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.06478\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.05636\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.05113\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.04496\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.02945\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.02518\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.03836\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.03491\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.03047\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.02440\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.02347\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.01943\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.01666\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01420\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01356\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.00974\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.00741\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00695\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00641\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00598\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00506\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00499\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00419\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00326\n",
-            " -- Time: 3.87 seconds\n",
-            " -- Linear: model.layers.8.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.18482\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16487\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15686\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13830\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08576\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07784\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10130\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09213\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08768\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07196\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06795\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05156\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04405\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04111\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04040\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02577\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02116\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02051\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01835\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01788\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01333\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01308\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01217\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00845\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.8.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.20113\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18273\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17538\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15457\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09416\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08692\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11035\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10015\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09581\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08035\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07590\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05670\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04819\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04548\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04482\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02840\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02389\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02333\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02116\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02074\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01511\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01540\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01422\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01094\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Layer: model.layers.8 (MLP)\n",
-            " -- Linear: model.layers.8.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16797\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15806\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15463\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14046\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07949\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07596\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08895\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08186\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08023\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07119\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06813\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04545\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03924\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03820\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03795\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02274\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01983\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01959\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01817\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01801\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01210\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01225\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01176\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00845\n",
-            " -- Time: 16.76 seconds\n",
-            " -- Linear: model.layers.8.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23683\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22306\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21845\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19867\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11225\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10738\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12523\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11508\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11321\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10052\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09621\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06397\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05514\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05385\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05353\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03199\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02772\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02740\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02534\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02515\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01690\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01674\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01646\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01114\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.8.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.21367\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19327\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18532\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16520\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09845\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09059\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11554\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10506\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10081\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08507\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08090\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05900\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05049\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04752\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04679\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02977\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02519\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02484\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02262\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02216\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01638\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01655\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01541\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01205\n",
-            " -- Time: 26.78 seconds\n",
-            " -- Layer: model.layers.9 (Attention)\n",
-            " -- Linear: model.layers.9.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09374\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08343\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07735\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06843\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04327\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03822\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05469\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04960\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04431\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03654\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03500\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02782\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02380\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02096\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02024\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01396\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01116\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01065\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00987\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00942\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00745\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00763\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00645\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00545\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Linear: model.layers.9.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.08149\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.07163\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.06561\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.05786\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03729\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03228\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.04811\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04369\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.03838\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03122\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03000\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02446\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02089\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01795\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01722\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01224\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.00937\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00884\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00818\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00768\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00637\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00628\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00533\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00413\n",
-            " -- Time: 3.79 seconds\n",
-            " -- Linear: model.layers.9.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19517\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17711\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17032\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15017\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09130\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08444\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10496\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09629\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09271\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07744\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07277\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05330\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04590\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04365\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04312\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02659\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02217\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02165\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01932\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01895\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01363\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01317\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01279\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00804\n",
-            " -- Time: 3.80 seconds\n",
-            " -- Linear: model.layers.9.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.20153\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18239\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17453\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15316\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09457\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08706\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11063\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10074\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09616\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07951\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07566\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05678\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04841\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04560\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04491\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02841\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02377\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02318\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02077\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02032\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01498\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01511\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01403\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01042\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Layer: model.layers.9 (MLP)\n",
-            " -- Linear: model.layers.9.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16709\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15677\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15316\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13897\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07902\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07526\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08855\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08146\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07979\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07048\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06740\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04528\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03912\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03803\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03776\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02269\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01986\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01961\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01817\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01801\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01216\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01244\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01179\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00879\n",
-            " -- Time: 16.77 seconds\n",
-            " -- Linear: model.layers.9.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23248\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21835\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21353\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19385\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10999\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10496\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12290\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11303\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11108\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09819\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09389\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06283\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05418\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05282\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05250\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03146\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02733\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02699\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02495\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02474\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01676\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01671\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01630\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01137\n",
-            " -- Time: 16.69 seconds\n",
-            " -- Linear: model.layers.9.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.22017\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19862\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19033\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16911\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10144\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09320\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11938\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10841\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10391\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08730\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08272\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06067\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05196\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04883\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04807\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03050\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02563\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02526\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02287\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02239\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01654\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01652\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01550\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01166\n",
-            " -- Time: 26.75 seconds\n",
-            " -- Layer: model.layers.10 (Attention)\n",
-            " -- Linear: model.layers.10.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.08235\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.07363\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.06866\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06023\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03814\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03398\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.04728\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04319\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.03894\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03206\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03048\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02402\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02064\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01838\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01782\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01202\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.00962\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00919\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00842\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00805\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00633\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00634\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00553\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00428\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Linear: model.layers.10.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.07516\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.06653\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.06168\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.05393\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03452\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03046\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.04334\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.03936\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.03536\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.02875\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.02734\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02195\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.01879\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01658\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01603\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01097\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.00858\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00817\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00742\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00705\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00568\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00556\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00487\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00357\n",
-            " -- Time: 3.77 seconds\n",
-            " -- Linear: model.layers.10.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.18187\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16366\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15689\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13720\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08496\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07789\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09894\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08995\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08648\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07118\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06690\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05035\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04296\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04066\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04012\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02514\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02081\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02025\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01800\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01762\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01297\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01259\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01205\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00796\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.10.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19772\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17857\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17053\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14967\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09223\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08466\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10913\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09919\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09394\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07816\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07389\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05624\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04783\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04464\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04390\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02824\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02368\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02304\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02093\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02045\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01527\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01561\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01425\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01133\n",
-            " -- Time: 6.18 seconds\n",
-            " -- Layer: model.layers.10 (MLP)\n",
-            " -- Linear: model.layers.10.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16458\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15345\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.14950\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13525\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07763\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07360\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08755\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08045\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07854\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06883\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06571\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04474\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03864\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03735\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03705\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02242\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01952\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01924\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01778\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01760\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01199\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01228\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01155\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00867\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.10.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22519\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21022\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20510\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18548\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10629\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10090\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11927\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10970\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10747\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09419\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08985\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06086\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05254\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05097\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05060\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03043\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02627\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02590\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02382\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02359\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01601\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01597\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01546\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01065\n",
-            " -- Time: 16.67 seconds\n",
-            " -- Linear: model.layers.10.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.23070\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20868\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20041\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17776\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10675\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09841\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12502\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11343\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10921\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09194\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08704\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06390\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05452\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05149\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05076\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03217\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02720\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02684\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02433\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02387\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01749\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01771\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01646\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01279\n",
-            " -- Time: 26.77 seconds\n",
-            " -- Layer: model.layers.11 (Attention)\n",
-            " -- Linear: model.layers.11.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09637\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08586\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08008\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07014\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04477\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03986\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05541\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05047\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04572\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03744\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03559\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02819\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02421\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02166\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02103\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01414\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01146\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01098\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01004\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00963\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00750\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00769\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00661\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00542\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Linear: model.layers.11.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.08114\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.07195\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.06606\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.05768\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03737\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03267\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.04767\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04342\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.03824\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03114\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.02972\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02425\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02075\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01802\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01732\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01210\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.00943\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00893\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00821\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00775\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00635\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00631\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00541\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00423\n",
-            " -- Time: 3.82 seconds\n",
-            " -- Linear: model.layers.11.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19703\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17551\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16728\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14535\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09159\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08328\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10811\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09762\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09342\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07590\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07111\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05511\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04670\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04391\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04323\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02758\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02246\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02183\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01924\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01879\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01423\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01368\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01300\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00864\n",
-            " -- Time: 3.92 seconds\n",
-            " -- Linear: model.layers.11.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19597\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17847\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16379\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14511\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09237\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08126\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12329\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11030\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09394\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07962\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07759\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06377\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05341\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04512\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04298\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03193\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02434\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02296\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02192\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02058\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01710\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01736\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01424\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01256\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Layer: model.layers.11 (MLP)\n",
-            " -- Linear: model.layers.11.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16838\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15674\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15241\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13774\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07955\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07520\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09017\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08269\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08051\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07030\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06713\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04617\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03980\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03838\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03804\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02317\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02025\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01994\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01844\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01824\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01252\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01300\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01203\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00944\n",
-            " -- Time: 16.79 seconds\n",
-            " -- Linear: model.layers.11.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22740\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21181\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20640\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18641\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10722\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10159\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12081\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11093\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10846\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09479\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09032\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06150\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05311\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05143\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05102\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03077\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02649\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02609\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02397\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02371\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01611\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01609\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01551\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01069\n",
-            " -- Time: 16.77 seconds\n",
-            " -- Linear: model.layers.11.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.22960\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20772\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19942\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17705\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10644\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09805\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12519\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11322\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10894\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09175\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08694\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06400\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05447\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05138\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05062\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03219\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02714\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02677\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02429\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02381\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01739\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01767\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01632\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01276\n",
-            " -- Time: 26.84 seconds\n",
-            " -- Layer: model.layers.12 (Attention)\n",
-            " -- Linear: model.layers.12.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10068\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08938\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08243\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07225\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04673\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04101\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06037\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05396\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04786\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03913\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03765\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03088\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02602\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02269\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02185\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01557\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01216\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01156\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01071\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01017\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00838\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00843\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00714\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00606\n",
-            " -- Time: 6.25 seconds\n",
-            " -- Linear: model.layers.12.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.08574\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.07553\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.06922\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06050\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03941\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03421\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05104\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04590\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04042\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03277\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03136\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02597\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02200\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01905\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01829\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01302\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01004\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00951\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00876\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00826\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00686\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00683\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00579\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00469\n",
-            " -- Time: 3.85 seconds\n",
-            " -- Linear: model.layers.12.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19504\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17522\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16722\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14655\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09135\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08336\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10798\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09762\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09299\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07656\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07215\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05508\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04669\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04381\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04310\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02753\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02244\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02178\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01939\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01891\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01422\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01369\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01304\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00864\n",
-            " -- Time: 3.84 seconds\n",
-            " -- Linear: model.layers.12.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.21198\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18789\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17851\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15663\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09908\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08925\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11771\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10642\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10141\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08235\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07812\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06074\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05148\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04812\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04730\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03056\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02559\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02480\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02233\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02180\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01657\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01693\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01547\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01239\n",
-            " -- Time: 6.23 seconds\n",
-            " -- Layer: model.layers.12 (MLP)\n",
-            " -- Linear: model.layers.12.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.17317\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16089\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15653\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14139\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08192\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07739\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09302\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08500\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08295\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07225\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06919\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04771\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04103\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03965\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03932\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02403\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02109\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02078\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01922\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01902\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01327\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01375\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01282\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01025\n",
-            " -- Time: 16.80 seconds\n",
-            " -- Linear: model.layers.12.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23561\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21922\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21368\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19274\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11136\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10544\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12512\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11497\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11266\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09816\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09356\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06397\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05511\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05348\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05310\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03199\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02766\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02727\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02501\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02476\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01693\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01695\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01637\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01149\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.12.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.23220\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20919\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19921\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17615\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10738\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09804\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12854\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11635\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.11008\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09202\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08705\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06542\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05593\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05194\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05095\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03304\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02763\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02712\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02468\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02405\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01806\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01837\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01671\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01338\n",
-            " -- Time: 26.78 seconds\n",
-            " -- Layer: model.layers.13 (Attention)\n",
-            " -- Linear: model.layers.13.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10346\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09299\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08766\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07699\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04820\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04354\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05839\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05322\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04910\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04058\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03850\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02967\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02545\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02319\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02264\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01485\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01207\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01163\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01055\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01019\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00778\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00777\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00696\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00522\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Linear: model.layers.13.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.08538\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.07664\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07156\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06280\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03961\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03533\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.04917\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04484\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04036\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03339\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03179\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02494\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02147\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01905\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01847\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01246\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.00989\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00945\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00865\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00825\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00651\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00643\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00569\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00422\n",
-            " -- Time: 3.82 seconds\n",
-            " -- Linear: model.layers.13.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.21169\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19023\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18235\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15968\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09888\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09079\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11536\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10443\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10062\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08274\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07782\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05883\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04983\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04732\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04671\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02938\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02406\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02345\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02073\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02031\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01508\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01435\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01392\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00878\n",
-            " -- Time: 3.81 seconds\n",
-            " -- Linear: model.layers.13.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22985\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20997\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20194\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18065\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10763\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09973\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12558\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11441\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10930\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09282\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08926\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06461\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05522\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05212\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05139\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03240\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02762\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02700\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02472\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02425\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01745\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01809\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01647\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01319\n",
-            " -- Time: 6.19 seconds\n",
-            " -- Layer: model.layers.13 (MLP)\n",
-            " -- Linear: model.layers.13.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.17483\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16251\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15795\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14266\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08279\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07819\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09385\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08610\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08379\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07304\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06979\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04815\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04151\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03999\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03964\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02414\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02113\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02080\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01922\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01901\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01303\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01362\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01251\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00993\n",
-            " -- Time: 16.77 seconds\n",
-            " -- Linear: model.layers.13.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23982\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22323\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21751\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19635\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11347\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10742\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12767\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11723\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11476\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10007\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09532\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06526\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05620\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05448\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05408\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03266\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02815\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02773\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02544\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02519\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01729\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01721\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01670\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01159\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.13.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.24641\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22109\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21158\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18667\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11395\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.10431\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13424\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12162\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.11690\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09716\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09181\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06867\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05844\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05492\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05408\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03458\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02889\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02847\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02562\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02507\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01873\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01869\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01756\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01328\n",
-            " -- Time: 26.78 seconds\n",
-            " -- Layer: model.layers.14 (Attention)\n",
-            " -- Linear: model.layers.14.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10582\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09534\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08908\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07862\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04931\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04419\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06149\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05579\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05026\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04186\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04004\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03130\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02671\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02382\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02309\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01568\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01253\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01200\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01107\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01060\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00827\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00834\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00722\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00576\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Linear: model.layers.14.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.08561\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.07738\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07080\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06264\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03965\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03475\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05197\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04725\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04042\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03395\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03267\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02629\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02257\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01913\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01824\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01316\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01000\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00941\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00888\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00832\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00689\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00678\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00572\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00448\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.14.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22144\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19968\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19127\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16781\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10366\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09508\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12296\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11022\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10547\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08733\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08233\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06295\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05278\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04971\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04898\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03148\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02549\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02481\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02217\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02167\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01627\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01558\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01481\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00998\n",
-            " -- Time: 3.84 seconds\n",
-            " -- Linear: model.layers.14.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24642\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21851\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20690\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18059\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11514\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10416\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13897\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12539\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11840\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09537\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09121\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.07166\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.06063\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05589\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05472\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03587\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02969\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02881\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02585\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02511\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01920\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01975\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01767\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01432\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Layer: model.layers.14 (MLP)\n",
-            " -- Linear: model.layers.14.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16493\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15358\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.14900\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13483\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07826\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07376\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08938\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08197\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07921\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06925\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06623\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04587\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03957\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03788\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03748\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02303\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02014\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01981\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01840\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01816\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01248\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01319\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01191\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00978\n",
-            " -- Time: 16.75 seconds\n",
-            " -- Linear: model.layers.14.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23642\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22076\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21511\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19454\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11210\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10626\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12655\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11623\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11338\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09929\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09471\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06476\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05580\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05394\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05350\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03243\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02805\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02763\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02549\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02521\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01728\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01745\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01663\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01207\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.14.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.23204\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20811\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19811\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17565\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10718\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09761\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12842\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11596\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10998\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09171\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08705\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06573\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05587\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05206\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05104\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03340\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02796\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02744\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02504\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02438\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01849\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01893\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01699\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01411\n",
-            " -- Time: 26.80 seconds\n",
-            " -- Layer: model.layers.15 (Attention)\n",
-            " -- Linear: model.layers.15.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11824\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10740\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.10090\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08950\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05527\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04995\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06887\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06209\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05624\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04743\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04546\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03518\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02984\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02678\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02602\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01771\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01425\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01369\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01273\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01224\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00947\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00962\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00832\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00688\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Linear: model.layers.15.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.08997\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08158\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07426\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06600\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04151\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03625\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05554\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05021\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04234\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03593\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03491\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02827\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02404\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02007\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01901\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01418\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01052\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00984\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00942\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00876\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00742\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00723\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00604\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00478\n",
-            " -- Time: 3.78 seconds\n",
-            " -- Linear: model.layers.15.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23763\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21808\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21011\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18801\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11228\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10438\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13130\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11890\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11398\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09722\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09255\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06716\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05694\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05385\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05308\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03358\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02751\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02687\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02442\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02393\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01729\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01660\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01602\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01042\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.15.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22281\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20108\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19287\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16640\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10428\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09603\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12125\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10998\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10599\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08663\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08045\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06233\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05283\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05024\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04962\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03121\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02622\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02562\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02269\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02227\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01662\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01658\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01577\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01154\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Layer: model.layers.15 (MLP)\n",
-            " -- Linear: model.layers.15.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16069\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15021\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.14602\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13239\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07632\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07225\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08692\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07965\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07721\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06788\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06506\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04466\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03849\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03699\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03663\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02248\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01974\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01943\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01811\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01790\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01236\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01298\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01187\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00972\n",
-            " -- Time: 16.77 seconds\n",
-            " -- Linear: model.layers.15.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23888\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22394\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21869\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19820\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11323\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10775\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12727\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11700\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11440\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10084\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09639\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06496\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05602\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05432\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05392\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03248\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02794\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02754\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02541\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02515\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01710\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01689\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01651\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01114\n",
-            " -- Time: 16.71 seconds\n",
-            " -- Linear: model.layers.15.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.22929\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20544\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19581\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17408\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10567\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09612\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12543\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11389\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10852\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09041\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08577\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06395\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05471\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05095\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05003\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03227\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02688\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02643\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02392\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02334\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01763\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01757\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01639\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01254\n",
-            " -- Time: 26.78 seconds\n",
-            " -- Layer: model.layers.16 (Attention)\n",
-            " -- Linear: model.layers.16.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11746\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10737\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.10179\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.09062\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05488\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.05019\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06660\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06065\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05572\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04742\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04533\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03389\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02900\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02641\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02577\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01698\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01376\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01328\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01228\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01187\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00893\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00889\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00799\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00601\n",
-            " -- Time: 6.24 seconds\n",
-            " -- Linear: model.layers.16.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09286\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08501\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07839\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07006\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04299\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03820\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05563\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05094\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04370\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03752\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03612\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02812\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02428\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02069\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01978\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01404\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01075\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01012\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00966\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00904\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00733\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00717\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00613\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00456\n",
-            " -- Time: 3.89 seconds\n",
-            " -- Linear: model.layers.16.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24342\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22526\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21816\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19590\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11499\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10791\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13360\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12081\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11661\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10061\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09606\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06830\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05777\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05512\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05448\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03414\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02804\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02746\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02504\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02462\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01754\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01666\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01634\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01022\n",
-            " -- Time: 3.85 seconds\n",
-            " -- Linear: model.layers.16.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23516\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21166\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20336\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18091\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10959\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10060\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12675\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11556\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11176\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09287\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08821\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06515\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05577\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05310\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05242\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03287\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02825\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02758\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02502\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02460\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01800\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01859\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01714\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01375\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Layer: model.layers.16 (MLP)\n",
-            " -- Linear: model.layers.16.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.15193\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.14219\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.13810\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.12526\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07207\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.06821\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08244\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07556\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07291\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06421\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06152\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04227\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03647\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03490\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03452\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02124\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01855\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01824\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01703\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01680\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01156\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01215\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01102\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00899\n",
-            " -- Time: 16.85 seconds\n",
-            " -- Linear: model.layers.16.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23238\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21834\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21318\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19339\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11013\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10490\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12404\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11403\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11123\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09835\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09399\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06329\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05459\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05284\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05242\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03164\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02718\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02679\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02477\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02451\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01664\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01645\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01604\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01085\n",
-            " -- Time: 16.73 seconds\n",
-            " -- Linear: model.layers.16.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.22319\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19859\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18842\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16736\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10245\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09253\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12249\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11134\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10546\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08704\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08280\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06257\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05346\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04942\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04840\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03161\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02606\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02557\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02307\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02243\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01725\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01710\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01591\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01215\n",
-            " -- Time: 26.79 seconds\n",
-            " -- Layer: model.layers.17 (Attention)\n",
-            " -- Linear: model.layers.17.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09829\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09000\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08431\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07568\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04579\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04136\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05741\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05237\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04662\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04001\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03857\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02920\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02505\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02210\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02135\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01463\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01162\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01111\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01049\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01001\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00768\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00777\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00663\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00532\n",
-            " -- Time: 6.19 seconds\n",
-            " -- Linear: model.layers.17.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.08335\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.07599\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.06954\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06252\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.03844\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03384\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05100\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04629\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.03928\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03366\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03269\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02575\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02205\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.01855\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01764\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01288\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.00970\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00911\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00875\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00817\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00672\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00661\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00549\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00433\n",
-            " -- Time: 3.90 seconds\n",
-            " -- Linear: model.layers.17.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.20309\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18364\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17341\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15613\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09403\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08522\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11704\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10452\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09631\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08153\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07840\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05969\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04993\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04516\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04397\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02979\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02319\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02230\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02063\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01985\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01531\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01458\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01324\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00917\n",
-            " -- Time: 3.81 seconds\n",
-            " -- Linear: model.layers.17.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22796\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20518\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19632\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17325\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10651\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09758\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12522\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11352\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10867\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09042\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08499\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06454\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05483\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05163\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05087\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03239\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02747\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02676\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02437\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02388\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01756\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01816\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01653\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01336\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Layer: model.layers.17 (MLP)\n",
-            " -- Linear: model.layers.17.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.15907\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.14871\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.14446\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13126\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07530\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07126\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08597\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07891\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07619\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06721\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06452\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04402\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03805\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03643\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03605\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02213\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01933\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01901\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01776\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01752\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01206\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01261\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01151\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00929\n",
-            " -- Time: 16.79 seconds\n",
-            " -- Linear: model.layers.17.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24069\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22613\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.22082\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.20047\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11399\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10863\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12831\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11795\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11515\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10196\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09775\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06561\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05659\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05480\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05438\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03293\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02847\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02808\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02606\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02580\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01763\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01763\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01703\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01214\n",
-            " -- Time: 16.71 seconds\n",
-            " -- Linear: model.layers.17.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.23679\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20960\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19847\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17539\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10882\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09788\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13094\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11836\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.11219\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09183\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08713\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06688\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05691\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05259\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05153\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03377\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02791\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02738\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02464\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02395\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01851\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01852\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01705\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01337\n",
-            " -- Time: 26.73 seconds\n",
-            " -- Layer: model.layers.18 (Attention)\n",
-            " -- Linear: model.layers.18.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11366\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10467\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09871\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08852\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05320\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04850\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06644\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06008\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05405\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04669\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04498\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03393\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02889\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02578\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02500\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01706\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01370\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01318\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01243\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01196\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00911\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00927\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00797\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00663\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Linear: model.layers.18.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09304\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08449\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07639\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06885\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04281\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03714\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05762\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05275\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04376\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03751\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03660\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02914\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02510\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02066\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01948\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01457\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01083\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01007\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00979\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00904\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00763\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00751\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00612\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00488\n",
-            " -- Time: 3.87 seconds\n",
-            " -- Linear: model.layers.18.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22252\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20193\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19171\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17346\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10408\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09482\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12835\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11432\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10636\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09040\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08736\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06581\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05469\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04999\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04883\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03301\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02564\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02473\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02284\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02208\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01701\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01596\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01485\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01001\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.18.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19772\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17592\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16816\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14555\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09180\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08353\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10693\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09711\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09339\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07592\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07098\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05513\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04712\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04478\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04419\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02785\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02420\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02365\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02121\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02086\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01553\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01642\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01477\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01263\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Layer: model.layers.18 (MLP)\n",
-            " -- Linear: model.layers.18.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.14728\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.13762\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.13347\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.12128\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.06964\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.06577\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07982\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07330\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07050\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06210\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.05950\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04083\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03526\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03363\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03324\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02047\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01771\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01739\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01623\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01599\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01103\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01140\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01048\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00820\n",
-            " -- Time: 16.83 seconds\n",
-            " -- Linear: model.layers.18.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22881\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21485\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20956\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19036\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10830\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10303\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12241\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11246\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10942\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09681\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09261\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06251\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05390\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05203\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05159\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03132\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02690\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02650\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02458\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02430\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01667\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01650\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01603\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01111\n",
-            " -- Time: 16.73 seconds\n",
-            " -- Linear: model.layers.18.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.22510\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19820\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18676\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16481\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10319\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09216\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12568\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11338\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10659\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08683\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08259\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06434\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05471\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05003\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04886\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03262\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02680\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02623\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02369\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02295\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01807\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01818\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01649\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01341\n",
-            " -- Time: 26.79 seconds\n",
-            " -- Layer: model.layers.19 (Attention)\n",
-            " -- Linear: model.layers.19.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11644\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10768\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.10152\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.09162\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05452\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04974\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06825\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06184\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05541\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04821\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04669\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03480\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02965\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02633\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02550\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01744\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01385\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01330\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01260\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01210\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00928\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00923\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00810\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00638\n",
-            " -- Time: 6.23 seconds\n",
-            " -- Linear: model.layers.19.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09202\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08449\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07618\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06889\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04248\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03691\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05797\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05307\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04333\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03766\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03691\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02941\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02524\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02054\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01927\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01472\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01077\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00997\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00982\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00903\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00773\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00752\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00611\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00488\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.19.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23251\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21308\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20404\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18453\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10898\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10053\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13124\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11786\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11101\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09542\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09173\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06728\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05643\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05229\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05128\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03361\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02663\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02584\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02383\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02317\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01721\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01620\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01532\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00993\n",
-            " -- Time: 3.84 seconds\n",
-            " -- Linear: model.layers.19.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.21240\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19376\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18742\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16420\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09921\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09250\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11509\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10345\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10047\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08449\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07993\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05928\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05036\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04845\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04797\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03000\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02635\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02590\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02359\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02329\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01684\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01795\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01624\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01403\n",
-            " -- Time: 6.19 seconds\n",
-            " -- Layer: model.layers.19 (MLP)\n",
-            " -- Linear: model.layers.19.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.14325\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.13404\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.13019\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.11838\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.06772\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.06406\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07722\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07102\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.06852\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06046\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.05793\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03943\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03415\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03266\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03230\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01978\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01714\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01685\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01571\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01549\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01061\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01094\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01011\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00779\n",
-            " -- Time: 16.78 seconds\n",
-            " -- Linear: model.layers.19.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22417\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21056\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20544\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18681\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10609\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10102\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11969\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11008\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10719\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09500\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09090\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06112\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05278\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05099\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05057\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03066\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02648\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02611\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02426\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02399\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01647\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01641\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01587\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01129\n",
-            " -- Time: 16.74 seconds\n",
-            " -- Linear: model.layers.19.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.21211\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18632\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17439\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15341\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09733\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.08616\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12003\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10839\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10058\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08150\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07764\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06150\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05253\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04730\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04602\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03137\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02558\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02490\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02262\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02176\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01772\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01772\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01604\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01321\n",
-            " -- Time: 26.78 seconds\n",
-            " -- Layer: model.layers.20 (Attention)\n",
-            " -- Linear: model.layers.20.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11630\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10727\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09987\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.09052\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05425\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04880\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07019\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06342\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05525\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04814\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04683\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03576\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03035\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02627\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02523\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01794\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01393\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01325\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01273\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01209\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00956\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00953\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00808\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00664\n",
-            " -- Time: 6.23 seconds\n",
-            " -- Linear: model.layers.20.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09284\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08449\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07454\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06771\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04260\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03601\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06061\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05522\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04370\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03771\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03725\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03066\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02624\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02060\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01910\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01531\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01089\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.00993\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00995\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00897\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00802\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00785\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00608\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00510\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.20.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23120\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20926\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19857\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17941\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10756\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09769\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13221\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11821\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10996\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09330\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09015\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06796\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05649\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05160\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05039\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03402\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02643\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02547\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02351\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02271\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01746\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01641\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01515\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01021\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.20.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23250\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20446\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19377\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16236\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10847\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09762\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12756\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11605\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11059\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08639\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08040\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06563\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05604\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05256\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05170\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03302\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02801\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02722\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02378\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02324\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01788\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01861\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01677\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01376\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Layer: model.layers.20 (MLP)\n",
-            " -- Linear: model.layers.20.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.15034\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.14114\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.13758\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.12504\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07114\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.06767\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08029\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07404\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07192\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06368\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06092\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04098\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03555\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03425\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03395\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02052\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01788\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01761\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01639\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01620\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01092\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01124\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01049\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00788\n",
-            " -- Time: 16.78 seconds\n",
-            " -- Linear: model.layers.20.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23300\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21932\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21445\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19492\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11020\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10522\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12351\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11371\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11129\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09876\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09448\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06288\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05443\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05285\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05248\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03154\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02717\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02682\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02484\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02461\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01673\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01640\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01619\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01081\n",
-            " -- Time: 16.73 seconds\n",
-            " -- Linear: model.layers.20.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.21185\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18673\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17575\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15506\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09714\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.08663\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11739\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10685\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10019\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08144\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07747\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06004\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05138\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04694\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04584\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03042\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02490\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02435\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02188\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02117\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01677\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01660\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01531\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01191\n",
-            " -- Time: 26.79 seconds\n",
-            " -- Layer: model.layers.21 (Attention)\n",
-            " -- Linear: model.layers.21.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11127\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10196\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09483\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08600\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05187\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04645\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06690\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06061\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05291\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04576\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04454\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03412\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02899\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02507\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02407\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01714\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01319\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01253\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01199\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01136\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00913\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00893\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00773\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00607\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Linear: model.layers.21.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09378\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08510\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07577\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.06884\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04326\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03671\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06057\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05497\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04434\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03813\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03759\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03076\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02632\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02092\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.01945\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01535\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01100\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01008\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.00999\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00908\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00801\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00781\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00614\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00506\n",
-            " -- Time: 3.85 seconds\n",
-            " -- Linear: model.layers.21.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22163\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19857\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18638\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16824\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10239\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09164\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12891\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11509\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10520\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08830\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08537\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06626\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05503\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04911\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04761\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03326\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02517\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02403\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02228\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02129\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01708\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01587\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01435\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00975\n",
-            " -- Time: 3.84 seconds\n",
-            " -- Linear: model.layers.21.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23350\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20365\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19298\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16334\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10854\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09643\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12523\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11522\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11068\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08594\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07940\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06430\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05538\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05234\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05160\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03227\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02745\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02665\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02300\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02251\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01727\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01758\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01624\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01239\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Layer: model.layers.21 (MLP)\n",
-            " -- Linear: model.layers.21.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.14922\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.14035\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.13702\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.12456\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07072\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.06739\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07943\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07325\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07139\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06332\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06061\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04061\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03517\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03403\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03376\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02035\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01774\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01750\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01628\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01611\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01090\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01111\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01053\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00778\n",
-            " -- Time: 16.78 seconds\n",
-            " -- Linear: model.layers.21.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23998\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22618\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.22144\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.20132\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11365\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10871\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12682\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11691\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11471\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10196\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09745\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06472\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05598\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05453\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05419\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03238\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02806\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02771\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02568\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02546\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01715\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01695\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01667\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01124\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.21.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.21060\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18573\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17462\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15507\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09622\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.08574\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11685\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10651\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.09940\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08131\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07743\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05938\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05114\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04648\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04530\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03010\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02458\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02399\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02170\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02094\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01659\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01635\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01509\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01158\n",
-            " -- Time: 26.82 seconds\n",
-            " -- Layer: model.layers.22 (Attention)\n",
-            " -- Linear: model.layers.22.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11209\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10229\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09493\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08603\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05214\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04653\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06759\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06109\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05337\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04590\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04473\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03453\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02925\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02521\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02418\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01736\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01327\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01260\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01205\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01141\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00927\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00901\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00782\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00614\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Linear: model.layers.22.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10182\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09235\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08218\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07468\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04701\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03994\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06553\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05968\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04823\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04141\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04076\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03335\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02857\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02278\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02124\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01667\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01206\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01106\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01095\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00998\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00877\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00861\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00679\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00569\n",
-            " -- Time: 3.81 seconds\n",
-            " -- Linear: model.layers.22.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.21928\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19550\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18235\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16446\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10102\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08950\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12897\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11501\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10395\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08672\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08398\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06603\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05492\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04849\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04685\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03305\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02487\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02364\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02197\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02087\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01693\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01587\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01411\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00978\n",
-            " -- Time: 3.84 seconds\n",
-            " -- Linear: model.layers.22.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.21762\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18789\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17726\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15261\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10171\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09076\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11871\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10860\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10392\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08076\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07427\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06082\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05235\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04925\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04849\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03053\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02610\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02537\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02207\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02155\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01645\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01714\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01544\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01247\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Layer: model.layers.22 (MLP)\n",
-            " -- Linear: model.layers.22.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16317\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15349\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.14991\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13635\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07727\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07367\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08670\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07996\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07803\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06923\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06627\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04433\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03836\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03715\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03686\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02217\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01930\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01903\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01769\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01751\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01182\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01197\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01143\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00826\n",
-            " -- Time: 16.77 seconds\n",
-            " -- Linear: model.layers.22.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24401\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22991\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.22512\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.20468\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11556\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.11050\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12880\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11884\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11660\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10366\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09914\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06572\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05685\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05539\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05504\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03285\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02843\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02807\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02600\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02578\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01734\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01707\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01686\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01120\n",
-            " -- Time: 16.74 seconds\n",
-            " -- Linear: model.layers.22.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.21802\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19351\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18269\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16234\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10003\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.08978\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12078\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10997\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10301\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08488\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08076\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06133\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05274\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04827\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04717\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03104\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02547\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02492\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02254\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02183\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01701\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01682\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01556\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01191\n",
-            " -- Time: 26.81 seconds\n",
-            " -- Layer: model.layers.23 (Attention)\n",
-            " -- Linear: model.layers.23.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11380\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10433\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09742\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08835\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05306\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04771\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06784\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06138\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05416\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04680\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04541\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03455\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02936\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02560\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02463\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01730\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01337\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01272\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01213\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01151\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00914\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00890\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00778\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00591\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Linear: model.layers.23.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10156\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09196\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08329\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07560\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04688\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04059\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06309\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05749\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04804\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04111\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04020\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03200\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02746\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02265\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02140\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01605\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01187\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01105\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01073\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00993\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00838\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00821\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00671\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00536\n",
-            " -- Time: 3.80 seconds\n",
-            " -- Linear: model.layers.23.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23422\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21260\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20214\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18244\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10900\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09931\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13251\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11975\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11144\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09471\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09120\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06770\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05719\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05224\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05103\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03382\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02671\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02575\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02379\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02298\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01734\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01653\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01523\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01019\n",
-            " -- Time: 3.88 seconds\n",
-            " -- Linear: model.layers.23.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.21480\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18771\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17768\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14708\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09886\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08851\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11611\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10647\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10162\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07861\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07177\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05943\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05118\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04773\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04689\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02974\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02508\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02427\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02111\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02054\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01586\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01625\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01473\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01144\n",
-            " -- Time: 6.19 seconds\n",
-            " -- Layer: model.layers.23 (MLP)\n",
-            " -- Linear: model.layers.23.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.17054\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16037\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15662\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14237\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08076\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07698\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09058\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08349\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08155\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07227\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06921\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04622\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04005\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03883\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03853\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02320\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02017\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01990\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01847\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01829\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01246\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01250\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01206\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00863\n",
-            " -- Time: 16.78 seconds\n",
-            " -- Linear: model.layers.23.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24915\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.23466\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.22974\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.20875\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11804\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.11278\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13139\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12130\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11907\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10571\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.10097\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06703\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05804\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05656\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05621\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03350\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02898\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02862\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02646\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02623\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01764\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01732\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01715\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01125\n",
-            " -- Time: 16.71 seconds\n",
-            " -- Linear: model.layers.23.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.22415\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19883\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18751\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.16688\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10277\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09218\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12361\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11317\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10595\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08731\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08310\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06306\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05426\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04954\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04837\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03177\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02603\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02545\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02301\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02226\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01716\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01705\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01560\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01188\n",
-            " -- Time: 26.73 seconds\n",
-            " -- Layer: model.layers.24 (Attention)\n",
-            " -- Linear: model.layers.24.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.12260\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.11279\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.10554\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.09573\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05739\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.05178\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07278\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06617\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05844\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.05073\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04924\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03712\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03172\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02773\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02670\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01860\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01456\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01389\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01324\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01261\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00981\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00976\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00839\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00663\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Linear: model.layers.24.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10946\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10019\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09057\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08224\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05079\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04400\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06904\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06303\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05189\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04493\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04405\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03504\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03008\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02456\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02307\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01753\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01283\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01189\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01166\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01073\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00918\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00890\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00729\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00571\n",
-            " -- Time: 3.80 seconds\n",
-            " -- Linear: model.layers.24.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24243\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22225\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21292\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19248\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11347\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10475\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13544\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12257\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11565\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09936\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09544\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06901\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05849\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05435\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05331\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03449\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02771\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02689\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02482\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02415\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01768\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01686\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01590\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01036\n",
-            " -- Time: 3.81 seconds\n",
-            " -- Linear: model.layers.24.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.18462\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16712\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15897\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13305\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08657\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07936\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10264\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09414\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08770\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07102\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06521\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05243\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04509\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04164\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04079\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02619\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02157\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02092\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01845\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01788\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01375\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01365\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01258\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00905\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Layer: model.layers.24 (MLP)\n",
-            " -- Linear: model.layers.24.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.17165\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16135\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15763\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14326\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08135\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07753\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09113\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08407\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08214\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07274\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06955\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04653\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04032\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03910\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03881\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02330\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02027\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02000\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01854\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01836\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01242\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01249\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01202\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00855\n",
-            " -- Time: 16.76 seconds\n",
-            " -- Linear: model.layers.24.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.25261\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.23792\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.23302\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.21172\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11972\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.11447\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13373\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12299\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.12081\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10716\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.10251\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06826\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05892\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05744\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05708\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03423\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02952\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02916\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02696\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02674\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01830\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01776\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01782\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01172\n",
-            " -- Time: 16.70 seconds\n",
-            " -- Linear: model.layers.24.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.22909\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20351\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19234\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17107\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10504\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09446\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12623\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11529\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10829\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08941\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08494\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06397\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05522\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05058\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04942\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03230\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02647\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02589\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02340\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02265\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01751\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01719\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01599\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01182\n",
-            " -- Time: 26.72 seconds\n",
-            " -- Layer: model.layers.25 (Attention)\n",
-            " -- Linear: model.layers.25.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.12900\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.11907\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.11197\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.10142\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.06049\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.05492\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07604\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06910\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.06151\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.05347\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.05184\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03876\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03306\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02918\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02820\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01940\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01522\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01456\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01382\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01321\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01017\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01003\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00875\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00668\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Linear: model.layers.25.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11070\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10151\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09247\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08384\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05146\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04495\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06873\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06295\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05250\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04551\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04442\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03481\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03008\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02483\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02343\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01742\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01292\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01203\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01174\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01086\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00904\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00883\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00729\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00564\n",
-            " -- Time: 3.85 seconds\n",
-            " -- Linear: model.layers.25.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24470\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22439\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21520\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19456\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11443\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10578\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13687\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12336\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11654\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10039\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09637\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06994\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05896\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05485\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05384\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03487\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02793\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02710\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02503\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02437\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01793\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01695\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01609\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01037\n",
-            " -- Time: 3.80 seconds\n",
-            " -- Linear: model.layers.25.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19971\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17495\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16666\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13776\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09312\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08423\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10770\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09795\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09494\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07392\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06606\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05482\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04697\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04471\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04418\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02750\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02313\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02253\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01925\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01888\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01463\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01430\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01390\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00958\n",
-            " -- Time: 6.19 seconds\n",
-            " -- Layer: model.layers.25 (MLP)\n",
-            " -- Linear: model.layers.25.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.17795\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16722\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16331\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14839\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08445\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08044\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09471\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08726\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08527\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07546\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07213\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04844\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04190\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04062\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04032\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02428\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02113\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02084\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01933\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01914\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01304\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01312\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01263\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00910\n",
-            " -- Time: 16.76 seconds\n",
-            " -- Linear: model.layers.25.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.25596\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.24084\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.23583\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.21412\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.12140\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.11602\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13501\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12474\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.12254\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10858\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.10364\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06887\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05970\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05820\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05784\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03440\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02980\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02943\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02717\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02694\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01798\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01778\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01748\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01151\n",
-            " -- Time: 16.68 seconds\n",
-            " -- Linear: model.layers.25.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.23103\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20522\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19355\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17225\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10588\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09500\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12759\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11703\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10922\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09041\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08588\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06501\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05611\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05101\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04974\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03263\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02675\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02612\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02371\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02290\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01746\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01750\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01578\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01207\n",
-            " -- Time: 26.74 seconds\n",
-            " -- Layer: model.layers.26 (Attention)\n",
-            " -- Linear: model.layers.26.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.12400\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.11394\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.10634\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.09626\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05808\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.05220\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07396\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06728\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05918\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.05122\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04970\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03773\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03227\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02804\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02695\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01888\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01467\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01396\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01331\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01263\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00986\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00981\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00834\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00655\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Linear: model.layers.26.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10733\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09786\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08772\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07956\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04976\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04267\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06874\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06260\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05088\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04381\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04301\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03479\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02981\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02406\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02250\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01739\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01260\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01160\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01143\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01045\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00913\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00881\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00717\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00566\n",
-            " -- Time: 3.83 seconds\n",
-            " -- Linear: model.layers.26.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23561\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21320\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20178\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18208\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10948\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09923\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13531\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12136\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11215\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09498\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09165\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06941\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05795\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05253\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05117\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03472\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02681\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02576\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02380\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02292\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01782\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01662\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01538\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01014\n",
-            " -- Time: 3.78 seconds\n",
-            " -- Linear: model.layers.26.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19595\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17731\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17136\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14596\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09234\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08536\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10379\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09552\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09358\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07667\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06999\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05305\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04578\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04433\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04400\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02652\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02284\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02238\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01966\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01943\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01391\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01389\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01344\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00925\n",
-            " -- Time: 6.19 seconds\n",
-            " -- Layer: model.layers.26 (MLP)\n",
-            " -- Linear: model.layers.26.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.18272\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.17161\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16761\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15209\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08677\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08269\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09719\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08969\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08762\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07741\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07390\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04969\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04302\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04170\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04140\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02485\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02162\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02132\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01974\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01954\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01320\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01332\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01277\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00911\n",
-            " -- Time: 16.80 seconds\n",
-            " -- Linear: model.layers.26.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.25765\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.24243\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.23726\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.21532\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.12240\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.11689\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13638\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12577\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.12352\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10932\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.10434\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06969\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.06023\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05870\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05834\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03484\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.03016\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02978\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02749\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02726\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01842\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01812\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01790\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01192\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.26.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.23600\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21045\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19898\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17697\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10846\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09767\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13012\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11931\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.11173\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09277\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08794\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06635\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05727\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05228\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05105\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03353\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02746\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02683\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02439\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02359\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01825\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01798\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01663\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01248\n",
-            " -- Time: 26.78 seconds\n",
-            " -- Layer: model.layers.27 (Attention)\n",
-            " -- Linear: model.layers.27.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.12800\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.11782\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.11131\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.10085\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.06014\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.05475\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07450\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06772\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.06119\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.05302\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.05126\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03807\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03246\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02901\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02815\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01906\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01516\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01454\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01374\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01318\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01001\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00993\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00873\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00666\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Linear: model.layers.27.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11138\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10262\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09586\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08676\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05208\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04693\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06588\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06038\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05305\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04602\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04448\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03345\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02885\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02511\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02417\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01676\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01307\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01244\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01185\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01125\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00880\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00864\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00750\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00566\n",
-            " -- Time: 3.84 seconds\n",
-            " -- Linear: model.layers.27.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23298\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21409\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20526\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18563\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10952\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10109\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12914\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11787\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11131\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09585\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09179\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06580\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05635\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05246\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05151\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03284\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02669\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02589\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02386\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02323\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01685\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01613\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01537\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00977\n",
-            " -- Time: 3.82 seconds\n",
-            " -- Linear: model.layers.27.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.21200\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.18792\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17930\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15069\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09955\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09040\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11465\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10507\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10141\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07998\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07405\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05895\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05073\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04826\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04768\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02975\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02572\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02513\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02197\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02157\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01632\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01694\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01555\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01257\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Layer: model.layers.27 (MLP)\n",
-            " -- Linear: model.layers.27.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.17764\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16664\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.16263\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.14745\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08444\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08030\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09481\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08744\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08529\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07525\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07172\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04845\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04203\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04066\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04033\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02426\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02116\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02086\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01932\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01911\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01290\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01317\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01245\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00915\n",
-            " -- Time: 16.77 seconds\n",
-            " -- Linear: model.layers.27.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.25108\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.23599\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.23094\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.20941\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11934\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.11386\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13301\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12278\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.12049\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10647\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.10147\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06792\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05881\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05724\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05687\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03394\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02938\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02900\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02675\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02651\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01784\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01762\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01731\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01152\n",
-            " -- Time: 16.71 seconds\n",
-            " -- Linear: model.layers.27.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.23620\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20981\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19752\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17574\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10841\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.09708\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13220\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12031\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.11186\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09251\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08790\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06722\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05766\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05227\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.05094\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03389\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02747\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02681\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02436\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02351\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01839\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01807\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01660\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01255\n",
-            " -- Time: 26.76 seconds\n",
-            " -- Layer: model.layers.28 (Attention)\n",
-            " -- Linear: model.layers.28.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11916\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10948\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.10109\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.09155\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05581\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04952\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07333\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06626\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05690\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04925\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04815\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03747\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03177\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02701\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02579\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01876\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01423\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01343\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01293\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01216\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00986\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00973\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00813\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00657\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Linear: model.layers.28.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10099\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09191\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08054\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07318\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04673\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03898\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06745\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06117\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04785\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04130\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04084\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03410\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02918\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02265\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02082\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01709\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01194\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01081\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01087\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00973\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00890\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00864\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00663\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00555\n",
-            " -- Time: 3.79 seconds\n",
-            " -- Linear: model.layers.28.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23685\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21366\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.20104\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.18104\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11007\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09898\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13777\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12351\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11284\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09523\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09198\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.07078\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05913\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05296\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05141\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03556\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02727\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02609\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02422\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02322\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01831\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01733\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01558\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01099\n",
-            " -- Time: 3.80 seconds\n",
-            " -- Linear: model.layers.28.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.22737\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19411\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18248\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15765\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10530\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09273\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12337\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11270\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10799\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08227\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07847\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06327\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05415\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05081\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04999\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03169\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02673\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02590\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02225\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02169\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01706\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01731\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01599\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01227\n",
-            " -- Time: 6.21 seconds\n",
-            " -- Layer: model.layers.28 (MLP)\n",
-            " -- Linear: model.layers.28.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16706\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15650\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15241\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13809\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07939\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07527\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08959\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08263\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08023\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07062\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06740\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04581\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03969\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03821\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03787\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02293\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01987\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01956\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01811\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01789\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01220\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01237\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01171\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00853\n",
-            " -- Time: 16.76 seconds\n",
-            " -- Linear: model.layers.28.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23511\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.22073\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.21568\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.19541\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11175\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.10642\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12497\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11535\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11284\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09957\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09490\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06392\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05531\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05369\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05329\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03199\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02771\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02733\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02525\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02500\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01694\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01690\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01639\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01135\n",
-            " -- Time: 16.70 seconds\n",
-            " -- Linear: model.layers.28.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.21678\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19135\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.17924\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15888\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09953\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.08835\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12181\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.11122\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10275\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08420\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07999\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06226\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05358\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04823\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04689\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03155\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02574\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02505\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02281\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02195\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01736\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01746\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01561\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01263\n",
-            " -- Time: 26.80 seconds\n",
-            " -- Layer: model.layers.29 (Attention)\n",
-            " -- Linear: model.layers.29.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11633\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10687\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09922\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08996\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05443\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04862\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07176\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06381\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05549\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04804\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04688\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03664\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03057\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02629\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02519\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01837\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01378\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01303\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01251\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01182\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00962\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00929\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00793\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00619\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Linear: model.layers.29.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10096\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09321\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08475\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07676\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04705\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04115\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06402\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05806\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04792\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04176\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04090\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03252\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02767\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02274\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02141\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01626\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01190\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01105\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01087\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01002\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00850\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00824\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00673\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00531\n",
-            " -- Time: 3.82 seconds\n",
-            " -- Linear: model.layers.29.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.25490\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.23516\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.22557\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.20400\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.12012\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.11134\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.14274\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12954\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.12215\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.10564\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.10147\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.07304\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.06200\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05764\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05657\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03666\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02949\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02865\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02653\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02583\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01884\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01809\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01694\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01133\n",
-            " -- Time: 3.78 seconds\n",
-            " -- Linear: model.layers.29.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.19766\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16867\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15821\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13995\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09188\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.08069\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10952\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09814\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.09429\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07310\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07000\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05625\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04722\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04440\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04370\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02818\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02332\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02257\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01963\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01917\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01500\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01505\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01398\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01065\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Layer: model.layers.29 (MLP)\n",
-            " -- Linear: model.layers.29.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16208\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15172\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.14757\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13356\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07702\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07297\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08709\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08036\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07788\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06844\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06527\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04455\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03862\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03711\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03676\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02231\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01938\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01907\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01768\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01745\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01190\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01221\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01140\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00855\n",
-            " -- Time: 16.78 seconds\n",
-            " -- Linear: model.layers.29.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.20992\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19685\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19202\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17416\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10051\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09569\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.11278\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10406\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10156\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08960\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08555\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05828\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05090\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04933\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04898\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02937\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02721\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02688\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02521\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02500\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01658\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01893\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01610\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01526\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.29.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.21573\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.19141\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.18033\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.15850\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.09927\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.08897\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.12029\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.10954\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.10214\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.08370\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.07890\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.06104\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05253\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04791\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04676\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03064\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02528\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02470\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02226\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02151\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01645\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01670\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01491\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01178\n",
-            " -- Time: 26.78 seconds\n",
-            " -- Layer: model.layers.30 (Attention)\n",
-            " -- Linear: model.layers.30.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11347\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.10390\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09477\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08558\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05303\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04638\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07177\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06453\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05413\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04664\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04581\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03667\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03100\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02572\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02433\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01841\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01363\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01274\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01238\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01151\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00972\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00955\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00781\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00648\n",
-            " -- Time: 6.23 seconds\n",
-            " -- Linear: model.layers.30.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09913\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09016\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07836\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07101\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04575\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03783\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06726\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06079\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04693\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04040\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04014\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03422\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02902\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02224\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02033\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01718\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01180\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01061\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01076\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00955\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00894\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00868\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00650\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00564\n",
-            " -- Time: 3.82 seconds\n",
-            " -- Linear: model.layers.30.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.23066\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.20681\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19312\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17402\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.10691\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09514\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.13683\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.12224\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.10968\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09215\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.08949\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.07042\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.05845\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05150\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.04971\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03542\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02662\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02530\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02364\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02248\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01832\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01728\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01517\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01106\n",
-            " -- Time: 3.81 seconds\n",
-            " -- Linear: model.layers.30.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.17670\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15877\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15307\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13045\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08273\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07668\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.09408\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08571\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08387\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06803\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06273\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04834\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04136\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03999\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03966\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02425\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02117\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02080\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01832\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01811\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01313\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01369\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01271\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01006\n",
-            " -- Time: 6.22 seconds\n",
-            " -- Layer: model.layers.30 (MLP)\n",
-            " -- Linear: model.layers.30.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.15936\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.14924\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.14528\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13162\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07624\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07227\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08603\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07933\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07705\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06784\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06488\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04448\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03879\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03740\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03707\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02246\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02056\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02028\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01901\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01882\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01267\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01425\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01223\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01139\n",
-            " -- Time: 16.81 seconds\n",
-            " -- Linear: model.layers.30.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.16603\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.15569\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15184\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13759\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07945\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.07553\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.08923\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.08227\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.08023\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07077\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06760\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04609\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04012\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03886\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03855\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02325\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02123\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02095\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01960\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01943\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01306\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01453\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01265\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01151\n",
-            " -- Time: 16.72 seconds\n",
-            " -- Linear: model.layers.30.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.18738\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.16703\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.15843\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.13782\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.08682\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.07855\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.10584\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.09386\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.08880\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.07284\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.06854\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.05334\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.04573\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.04253\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.04171\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02725\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02341\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.02301\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02086\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02037\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01563\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01646\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01464\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01297\n",
-            " -- Time: 26.74 seconds\n",
-            " -- Layer: model.layers.31 (Attention)\n",
-            " -- Linear: model.layers.31.self_attn.q_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.10866\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09916\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09031\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08169\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05073\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04421\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06838\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06180\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05183\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04453\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04367\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03496\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02965\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02457\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02324\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01751\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01299\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01213\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01178\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01095\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00921\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00908\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00739\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00612\n",
-            " -- Time: 6.20 seconds\n",
-            " -- Linear: model.layers.31.self_attn.k_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09790\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.08884\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.07772\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.07047\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04527\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.03773\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06637\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05931\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04648\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.03986\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03951\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03362\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02834\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02199\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02022\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01685\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01166\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01057\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01061\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.00950\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00885\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00850\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00657\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00558\n",
-            " -- Time: 3.81 seconds\n",
-            " -- Linear: model.layers.31.self_attn.v_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.24299\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.21556\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.19947\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.17985\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.11267\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.09866\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.14473\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.13010\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.11611\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.09605\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.09349\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.07436\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.06221\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.05421\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.05216\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.03738\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.02799\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.02645\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.02463\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.02326\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01925\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01820\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01589\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01143\n",
-            " -- Time: 3.81 seconds\n",
-            " -- Linear: model.layers.31.self_attn.o_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.11279\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09924\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.09478\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08162\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05092\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04609\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.06104\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.05349\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.05188\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04118\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03942\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03045\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02751\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02643\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02621\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01618\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01648\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01625\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01499\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01487\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01008\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01328\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00977\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.01199\n",
-            " -- Time: 6.19 seconds\n",
-            " -- Layer: model.layers.31 (MLP)\n",
-            " -- Linear: model.layers.31.mlp.gate_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.14756\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.13842\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.13503\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.12221\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.07013\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.06670\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07889\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.07267\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.07083\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.06240\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.05951\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.04030\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03487\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.03371\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.03344\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.02017\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01744\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01718\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01588\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01570\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01063\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01072\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.01024\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00726\n",
-            " -- Time: 16.81 seconds\n",
-            " -- Linear: model.layers.31.mlp.up_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.19 bpw    rfn_error: 0.09824\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.09194\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.08962\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.08108\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.04664\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.69 bpw    rfn_error: 0.04430\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.05260\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.04838\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.19 bpw    rfn_error: 0.04713\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.04143\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.03955\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.02695\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.02329\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02251\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.31 bpw    rfn_error: 0.02232\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01354\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01183\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.38 bpw    rfn_error: 0.01166\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01080\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01069\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.00736\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.00754\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.22 bpw    rfn_error: 0.00711\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00542\n",
-            " -- Time: 16.70 seconds\n",
-            " -- Linear: model.layers.31.mlp.down_proj\n",
-            " -- 0.05:3b/0.95:2b 32g s4         2.18 bpw    rfn_error: 0.12848\n",
-            " -- 0.25:3b/0.75:2b 32g s4         2.38 bpw    rfn_error: 0.11652\n",
-            " -- 0.25:4b/0.75:2b 32g s4         2.63 bpw    rfn_error: 0.11088\n",
-            " -- 0.1:4b/0.4:3b/0.5:2b 32g s4    2.72 bpw    rfn_error: 0.09626\n",
-            " -- 0.1:4b/0.9:3b 32g s4           3.22 bpw    rfn_error: 0.05991\n",
-            " -- 0.2:6b/0.8:3b 32g s4           3.71 bpw    rfn_error: 0.05494\n",
-            " -- 1.0:3b 128g s4                 3.03 bpw    rfn_error: 0.07339\n",
-            " -- 1.0:3b 32g s4                  3.13 bpw    rfn_error: 0.06539\n",
-            " -- 0.05:4b/0.95:3b 32g s4         3.18 bpw    rfn_error: 0.06116\n",
-            " -- 0.4:4b/0.6:3b 32g s4           3.53 bpw    rfn_error: 0.05120\n",
-            " -- 0.6:4b/0.4:3b 64g s4           3.66 bpw    rfn_error: 0.04834\n",
-            " -- 1.0:4b 128g s4                 4.03 bpw    rfn_error: 0.03777\n",
-            " -- 1.0:4b 32g s4                  4.13 bpw    rfn_error: 0.03195\n",
-            " -- 0.1:5b/0.9:4b 32g s4           4.22 bpw    rfn_error: 0.02955\n",
-            " -- 0.1:6b/0.9:4b 32g s4           4.32 bpw    rfn_error: 0.02893\n",
-            " -- 1.0:5b 128g s4                 5.03 bpw    rfn_error: 0.01943\n",
-            " -- 0.1:6b/0.9:5b 32g s4           5.22 bpw    rfn_error: 0.01655\n",
-            " -- 0.05:8b/0.05:6b/0.9:5b 32g s4  5.34 bpw    rfn_error: 0.01626\n",
-            " -- 0.4:6b/0.6:5b 32g s4           5.53 bpw    rfn_error: 0.01499\n",
-            " -- 0.1:8b/0.3:6b/0.6:5b 32g s4    5.72 bpw    rfn_error: 0.01465\n",
-            " -- 1.0:6b 128g s4                 6.03 bpw    rfn_error: 0.01136\n",
-            " -- 1.0:6b 32g s4                  6.13 bpw    rfn_error: 0.01197\n",
-            " -- 0.1:8b/0.9:6b 128g s4          6.23 bpw    rfn_error: 0.01058\n",
-            " -- 1.0:8b 32g s4                  8.13 bpw    rfn_error: 0.00968\n",
-            " -- Time: 26.78 seconds\n",
-            " -- Layer: model.norm (RMSNorm)\n",
-            " -- Layer: lm_head (Linear)\n",
-            " -- Calibration perplexity (base): 6.5756\n",
-            " -- Optimizing...\n",
-            " -- rfn max: 0.12882  bpw: 2.89358\n",
-            " -- rfn max: 0.06441  bpw: 3.83733\n",
-            " -- rfn max: 0.03221  bpw: 4.87716\n",
-            " -- rfn max: 0.01610  bpw: 6.44259\n",
-            " -- rfn max: 0.02420  bpw: 5.31034\n",
-            " -- rfn max: 0.02831  bpw: 4.98366\n",
-            " -- rfn max: 0.02625  bpw: 5.11053\n",
-            " -- rfn max: 0.02733  bpw: 5.03992\n",
-            " -- rfn max: 0.02792  bpw: 5.00472\n",
-            " -- rfn max: 0.02826  bpw: 4.98372\n",
-            " -- rfn max: 0.02809  bpw: 4.99354\n",
-            " -- rfn max: 0.02800  bpw: 4.99961\n",
-            " -- rfn max: 0.02796  bpw: 5.00233\n",
-            " -- rfn max: 0.02803  bpw: 4.99924\n",
-            " -- rfn max: 0.02800  bpw: 4.99961\n",
-            " -- rfn max: 0.02798  bpw: 5.00233\n",
-            " -- rfn max: 0.02804  bpw: 4.99880\n",
-            " -- rfn max: 0.02801  bpw: 4.99961\n",
-            " -- rfn max: 0.02799  bpw: 5.00093\n",
-            " -- rfn max: 0.02805  bpw: 4.99880\n",
-            " -- rfn max: 0.02802  bpw: 4.99924\n",
-            " -- rfn max: 0.02801  bpw: 4.99961\n",
-            " -- rfn max: 0.02800  bpw: 4.99961\n",
-            " -- rfn min: 0.01400  bpw: 5.89112\n",
-            " -- rfn min: 0.02100  bpw: 5.27758\n",
-            " -- rfn min: 0.02450  bpw: 5.11620\n",
-            " -- rfn min: 0.02625  bpw: 5.03063\n",
-            " -- rfn min: 0.02713  bpw: 5.00971\n",
-            " -- rfn min: 0.02756  bpw: 4.99961\n",
-            " -- rfn min: 0.02734  bpw: 5.00551\n",
-            " -- rfn min: 0.02745  bpw: 5.00093\n",
-            " -- rfn min: 0.02751  bpw: 5.00093\n",
-            " -- rfn min: 0.02754  bpw: 5.00093\n",
-            " -- rfn min: 0.02755  bpw: 4.99961\n",
-            " -- rfn min: 0.02754  bpw: 4.99961\n",
-            " -- Tokenizing samples...\n",
-            " -- First 50 tokens of dataset:\n",
-            "    ' = Robert Boulter = \\n  Robert Boulter is an English film , television and theatre actor . He had a guest @-@ starring role on the television series The Bill in 2000 . This was followed'\n",
-            " -- Last 50 tokens of dataset:\n",
-            "    '] more meaningful lives \" . The film argues the case against conformity , but does not deny that people need and want it ; even the gay characters just want to fit in . Jim and Jim , the Burnhams \\' other neighbors , are'\n",
-            " -- Token embeddings again...\n",
-            " -- Quantizing...\n",
-            " -- Layer: model.layers.0 (Attention)\n",
-            " -- Linear: model.layers.0.self_attn.q_proj -> 0.05:3b/0.95:2b 32g s4, 2.19 bpw\n",
-            " -- Linear: model.layers.0.self_attn.k_proj -> 0.05:3b/0.95:2b 32g s4, 2.19 bpw\n",
-            " -- Linear: model.layers.0.self_attn.v_proj -> 0.2:6b/0.8:3b 32g s4, 3.69 bpw\n",
-            " -- Linear: model.layers.0.self_attn.o_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Layer rfn_error: 0.022664\n",
-            " -- Module quantized, time: 15.82 seconds\n",
-            " -- Layer: model.layers.0 (MLP)\n",
-            " -- Linear: model.layers.0.mlp.gate_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.0.mlp.up_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.0.mlp.down_proj -> 0.6:4b/0.4:3b 64g s4, 3.66 bpw\n",
-            " -- Layer rfn_error: 0.030517\n",
-            " -- Module quantized, time: 39.55 seconds\n",
-            " -- Layer: model.layers.1 (Attention)\n",
-            " -- Linear: model.layers.1.self_attn.q_proj -> 0.05:3b/0.95:2b 32g s4, 2.19 bpw\n",
-            " -- Linear: model.layers.1.self_attn.k_proj -> 0.05:3b/0.95:2b 32g s4, 2.19 bpw\n",
-            " -- Linear: model.layers.1.self_attn.v_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.1.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.027920\n",
-            " -- Module quantized, time: 15.11 seconds\n",
-            " -- Layer: model.layers.1 (MLP)\n",
-            " -- Linear: model.layers.1.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.1.mlp.up_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.1.mlp.down_proj -> 0.05:3b/0.95:2b 32g s4, 2.18 bpw\n",
-            " -- Layer rfn_error: 0.019765\n",
-            " -- Module quantized, time: 38.87 seconds\n",
-            " -- Layer: model.layers.2 (Attention)\n",
-            " -- Linear: model.layers.2.self_attn.q_proj -> 0.05:4b/0.95:3b 32g s4, 3.19 bpw\n",
-            " -- Linear: model.layers.2.self_attn.k_proj -> 0.05:4b/0.95:3b 32g s4, 3.19 bpw\n",
-            " -- Linear: model.layers.2.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.2.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.001632\n",
-            " -- Module quantized, time: 15.01 seconds\n",
-            " -- Layer: model.layers.2 (MLP)\n",
-            " -- Linear: model.layers.2.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.2.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.2.mlp.down_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.001273\n",
-            " -- Module quantized, time: 40.94 seconds\n",
-            " -- Layer: model.layers.3 (Attention)\n",
-            " -- Linear: model.layers.3.self_attn.q_proj -> 1.0:3b 128g s4, 3.03 bpw\n",
-            " -- Linear: model.layers.3.self_attn.k_proj -> 0.1:4b/0.4:3b/0.5:2b 32g s4, 2.72 bpw\n",
-            " -- Linear: model.layers.3.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.3.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.002017\n",
-            " -- Module quantized, time: 14.93 seconds\n",
-            " -- Layer: model.layers.3 (MLP)\n",
-            " -- Linear: model.layers.3.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.3.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.3.mlp.down_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.001798\n",
-            " -- Module quantized, time: 39.25 seconds\n",
-            " -- Layer: model.layers.4 (Attention)\n",
-            " -- Linear: model.layers.4.self_attn.q_proj -> 0.1:4b/0.9:3b 32g s4, 3.22 bpw\n",
-            " -- Linear: model.layers.4.self_attn.k_proj -> 0.05:4b/0.95:3b 32g s4, 3.19 bpw\n",
-            " -- Linear: model.layers.4.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.4.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.002456\n",
-            " -- Module quantized, time: 15.05 seconds\n",
-            " -- Layer: model.layers.4 (MLP)\n",
-            " -- Linear: model.layers.4.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.4.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.4.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.002233\n",
-            " -- Module quantized, time: 39.20 seconds\n",
-            " -- Layer: model.layers.5 (Attention)\n",
-            " -- Linear: model.layers.5.self_attn.q_proj -> 0.4:4b/0.6:3b 32g s4, 3.53 bpw\n",
-            " -- Linear: model.layers.5.self_attn.k_proj -> 0.4:4b/0.6:3b 32g s4, 3.53 bpw\n",
-            " -- Linear: model.layers.5.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.5.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.002432\n",
-            " -- Module quantized, time: 15.09 seconds\n",
-            " -- Layer: model.layers.5 (MLP)\n",
-            " -- Linear: model.layers.5.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.5.mlp.up_proj -> 0.05:8b/0.05:6b/0.9:5b 32g s4, 5.38 bpw\n",
-            " -- Linear: model.layers.5.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.003064\n",
-            " -- Module quantized, time: 40.26 seconds\n",
-            " -- Layer: model.layers.6 (Attention)\n",
-            " -- Linear: model.layers.6.self_attn.q_proj -> 0.4:4b/0.6:3b 32g s4, 3.53 bpw\n",
-            " -- Linear: model.layers.6.self_attn.k_proj -> 0.1:4b/0.9:3b 32g s4, 3.22 bpw\n",
-            " -- Linear: model.layers.6.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.6.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.003424\n",
-            " -- Module quantized, time: 15.77 seconds\n",
-            " -- Layer: model.layers.6 (MLP)\n",
-            " -- Linear: model.layers.6.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.6.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.6.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.003627\n",
-            " -- Module quantized, time: 39.28 seconds\n",
-            " -- Layer: model.layers.7 (Attention)\n",
-            " -- Linear: model.layers.7.self_attn.q_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.7.self_attn.k_proj -> 0.4:4b/0.6:3b 32g s4, 3.53 bpw\n",
-            " -- Linear: model.layers.7.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.7.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.003550\n",
-            " -- Module quantized, time: 14.95 seconds\n",
-            " -- Layer: model.layers.7 (MLP)\n",
-            " -- Linear: model.layers.7.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.7.mlp.up_proj -> 0.05:8b/0.05:6b/0.9:5b 32g s4, 5.38 bpw\n",
-            " -- Linear: model.layers.7.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.004240\n",
-            " -- Module quantized, time: 39.07 seconds\n",
-            " -- Layer: model.layers.8 (Attention)\n",
-            " -- Linear: model.layers.8.self_attn.q_proj -> 0.6:4b/0.4:3b 64g s4, 3.66 bpw\n",
-            " -- Linear: model.layers.8.self_attn.k_proj -> 0.4:4b/0.6:3b 32g s4, 3.53 bpw\n",
-            " -- Linear: model.layers.8.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.8.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.004491\n",
-            " -- Module quantized, time: 14.88 seconds\n",
-            " -- Layer: model.layers.8 (MLP)\n",
-            " -- Linear: model.layers.8.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.8.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.8.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.004691\n",
-            " -- Module quantized, time: 41.15 seconds\n",
-            " -- Layer: model.layers.9 (Attention)\n",
-            " -- Linear: model.layers.9.self_attn.q_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.9.self_attn.k_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.9.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.9.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.005494\n",
-            " -- Module quantized, time: 14.82 seconds\n",
-            " -- Layer: model.layers.9 (MLP)\n",
-            " -- Linear: model.layers.9.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.9.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.9.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.005163\n",
-            " -- Module quantized, time: 39.37 seconds\n",
-            " -- Layer: model.layers.10 (Attention)\n",
-            " -- Linear: model.layers.10.self_attn.q_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.10.self_attn.k_proj -> 0.6:4b/0.4:3b 64g s4, 3.66 bpw\n",
-            " -- Linear: model.layers.10.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.10.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.006206\n",
-            " -- Module quantized, time: 14.98 seconds\n",
-            " -- Layer: model.layers.10 (MLP)\n",
-            " -- Linear: model.layers.10.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.10.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.10.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.005504\n",
-            " -- Module quantized, time: 41.28 seconds\n",
-            " -- Layer: model.layers.11 (Attention)\n",
-            " -- Linear: model.layers.11.self_attn.q_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.11.self_attn.k_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.11.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.11.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.007919\n",
-            " -- Module quantized, time: 15.37 seconds\n",
-            " -- Layer: model.layers.11 (MLP)\n",
-            " -- Linear: model.layers.11.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.11.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.11.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.005873\n",
-            " -- Module quantized, time: 39.38 seconds\n",
-            " -- Layer: model.layers.12 (Attention)\n",
-            " -- Linear: model.layers.12.self_attn.q_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.12.self_attn.k_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.12.self_attn.v_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.12.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.009183\n",
-            " -- Module quantized, time: 15.00 seconds\n",
-            " -- Layer: model.layers.12 (MLP)\n",
-            " -- Linear: model.layers.12.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.12.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.12.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.006568\n",
-            " -- Module quantized, time: 39.22 seconds\n",
-            " -- Layer: model.layers.13 (Attention)\n",
-            " -- Linear: model.layers.13.self_attn.q_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.13.self_attn.k_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.13.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.13.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.007230\n",
-            " -- Module quantized, time: 15.30 seconds\n",
-            " -- Layer: model.layers.13 (MLP)\n",
-            " -- Linear: model.layers.13.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.13.mlp.up_proj -> 0.05:8b/0.05:6b/0.9:5b 32g s4, 5.38 bpw\n",
-            " -- Linear: model.layers.13.mlp.down_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Layer rfn_error: 0.006850\n",
-            " -- Module quantized, time: 39.51 seconds\n",
-            " -- Layer: model.layers.14 (Attention)\n",
-            " -- Linear: model.layers.14.self_attn.q_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.14.self_attn.k_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.14.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.14.self_attn.o_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Layer rfn_error: 0.008164\n",
-            " -- Module quantized, time: 15.19 seconds\n",
-            " -- Layer: model.layers.14 (MLP)\n",
-            " -- Linear: model.layers.14.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.14.mlp.up_proj -> 0.05:8b/0.05:6b/0.9:5b 32g s4, 5.38 bpw\n",
-            " -- Linear: model.layers.14.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.007977\n",
-            " -- Module quantized, time: 39.93 seconds\n",
-            " -- Layer: model.layers.15 (Attention)\n",
-            " -- Linear: model.layers.15.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.15.self_attn.k_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.15.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.15.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.009676\n",
-            " -- Module quantized, time: 15.08 seconds\n",
-            " -- Layer: model.layers.15 (MLP)\n",
-            " -- Linear: model.layers.15.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.15.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.15.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.008644\n",
-            " -- Module quantized, time: 41.41 seconds\n",
-            " -- Layer: model.layers.16 (Attention)\n",
-            " -- Linear: model.layers.16.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.16.self_attn.k_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.16.self_attn.v_proj -> 0.05:8b/0.05:6b/0.9:5b 32g s4, 5.38 bpw\n",
-            " -- Linear: model.layers.16.self_attn.o_proj -> 0.05:8b/0.05:6b/0.9:5b 32g s4, 5.38 bpw\n",
-            " -- Layer rfn_error: 0.008491\n",
-            " -- Module quantized, time: 17.31 seconds\n",
-            " -- Layer: model.layers.16 (MLP)\n",
-            " -- Linear: model.layers.16.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.16.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.16.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.009762\n",
-            " -- Module quantized, time: 39.63 seconds\n",
-            " -- Layer: model.layers.17 (Attention)\n",
-            " -- Linear: model.layers.17.self_attn.q_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.17.self_attn.k_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.17.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.17.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.010878\n",
-            " -- Module quantized, time: 15.10 seconds\n",
-            " -- Layer: model.layers.17 (MLP)\n",
-            " -- Linear: model.layers.17.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.17.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.17.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.010737\n",
-            " -- Module quantized, time: 41.41 seconds\n",
-            " -- Layer: model.layers.18 (Attention)\n",
-            " -- Linear: model.layers.18.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.18.self_attn.k_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.18.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.18.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.010465\n",
-            " -- Module quantized, time: 15.01 seconds\n",
-            " -- Layer: model.layers.18 (MLP)\n",
-            " -- Linear: model.layers.18.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.18.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.18.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.012024\n",
-            " -- Module quantized, time: 39.66 seconds\n",
-            " -- Layer: model.layers.19 (Attention)\n",
-            " -- Linear: model.layers.19.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.19.self_attn.k_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.19.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.19.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.012131\n",
-            " -- Module quantized, time: 15.02 seconds\n",
-            " -- Layer: model.layers.19 (MLP)\n",
-            " -- Linear: model.layers.19.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.19.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.19.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.012679\n",
-            " -- Module quantized, time: 39.46 seconds\n",
-            " -- Layer: model.layers.20 (Attention)\n",
-            " -- Linear: model.layers.20.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.20.self_attn.k_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.20.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.20.self_attn.o_proj -> 0.05:8b/0.05:6b/0.9:5b 32g s4, 5.38 bpw\n",
-            " -- Layer rfn_error: 0.009064\n",
-            " -- Module quantized, time: 15.09 seconds\n",
-            " -- Layer: model.layers.20 (MLP)\n",
-            " -- Linear: model.layers.20.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.20.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.20.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.012641\n",
-            " -- Module quantized, time: 39.37 seconds\n",
-            " -- Layer: model.layers.21 (Attention)\n",
-            " -- Linear: model.layers.21.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.21.self_attn.k_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.21.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.21.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.008775\n",
-            " -- Module quantized, time: 15.28 seconds\n",
-            " -- Layer: model.layers.21 (MLP)\n",
-            " -- Linear: model.layers.21.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.21.mlp.up_proj -> 0.05:8b/0.05:6b/0.9:5b 32g s4, 5.38 bpw\n",
-            " -- Linear: model.layers.21.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.012627\n",
-            " -- Module quantized, time: 40.02 seconds\n",
-            " -- Layer: model.layers.22 (Attention)\n",
-            " -- Linear: model.layers.22.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.22.self_attn.k_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.22.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.22.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.005946\n",
-            " -- Module quantized, time: 15.25 seconds\n",
-            " -- Layer: model.layers.22 (MLP)\n",
-            " -- Linear: model.layers.22.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.22.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.22.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.012623\n",
-            " -- Module quantized, time: 41.70 seconds\n",
-            " -- Layer: model.layers.23 (Attention)\n",
-            " -- Linear: model.layers.23.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.23.self_attn.k_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.23.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.23.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.007499\n",
-            " -- Module quantized, time: 16.99 seconds\n",
-            " -- Layer: model.layers.23 (MLP)\n",
-            " -- Linear: model.layers.23.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.23.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.23.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.012987\n",
-            " -- Module quantized, time: 40.31 seconds\n",
-            " -- Layer: model.layers.24 (Attention)\n",
-            " -- Linear: model.layers.24.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.24.self_attn.k_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.24.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.24.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.007275\n",
-            " -- Module quantized, time: 15.06 seconds\n",
-            " -- Layer: model.layers.24 (MLP)\n",
-            " -- Linear: model.layers.24.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.24.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.24.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.013189\n",
-            " -- Module quantized, time: 39.56 seconds\n",
-            " -- Layer: model.layers.25 (Attention)\n",
-            " -- Linear: model.layers.25.self_attn.q_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.25.self_attn.k_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.25.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.25.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.007302\n",
-            " -- Module quantized, time: 15.12 seconds\n",
-            " -- Layer: model.layers.25 (MLP)\n",
-            " -- Linear: model.layers.25.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.25.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.25.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.013655\n",
-            " -- Module quantized, time: 44.16 seconds\n",
-            " -- Layer: model.layers.26 (Attention)\n",
-            " -- Linear: model.layers.26.self_attn.q_proj -> 0.1:6b/0.9:4b 32g s4, 4.31 bpw\n",
-            " -- Linear: model.layers.26.self_attn.k_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.26.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.26.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.007353\n",
-            " -- Module quantized, time: 15.06 seconds\n",
-            " -- Layer: model.layers.26 (MLP)\n",
-            " -- Linear: model.layers.26.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.26.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.26.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.013976\n",
-            " -- Module quantized, time: 40.31 seconds\n",
-            " -- Layer: model.layers.27 (Attention)\n",
-            " -- Linear: model.layers.27.self_attn.q_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.27.self_attn.k_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.27.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.27.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.005969\n",
-            " -- Module quantized, time: 15.99 seconds\n",
-            " -- Layer: model.layers.27 (MLP)\n",
-            " -- Linear: model.layers.27.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.27.mlp.up_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.27.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.014250\n",
-            " -- Module quantized, time: 39.24 seconds\n",
-            " -- Layer: model.layers.28 (Attention)\n",
-            " -- Linear: model.layers.28.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.28.self_attn.k_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.28.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.28.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.007878\n",
-            " -- Module quantized, time: 15.10 seconds\n",
-            " -- Layer: model.layers.28 (MLP)\n",
-            " -- Linear: model.layers.28.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.28.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.28.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.015297\n",
-            " -- Module quantized, time: 39.29 seconds\n",
-            " -- Layer: model.layers.29 (Attention)\n",
-            " -- Linear: model.layers.29.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.29.self_attn.k_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Linear: model.layers.29.self_attn.v_proj -> 0.4:6b/0.6:5b 32g s4, 5.53 bpw\n",
-            " -- Linear: model.layers.29.self_attn.o_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.011452\n",
-            " -- Module quantized, time: 15.05 seconds\n",
-            " -- Layer: model.layers.29 (MLP)\n",
-            " -- Linear: model.layers.29.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.29.mlp.up_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.29.mlp.down_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Layer rfn_error: 0.015456\n",
-            " -- Module quantized, time: 40.25 seconds\n",
-            " -- Layer: model.layers.30 (Attention)\n",
-            " -- Linear: model.layers.30.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.30.self_attn.k_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.30.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.30.self_attn.o_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.009950\n",
-            " -- Module quantized, time: 14.98 seconds\n",
-            " -- Layer: model.layers.30 (MLP)\n",
-            " -- Linear: model.layers.30.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.30.mlp.up_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.30.mlp.down_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.019067\n",
-            " -- Module quantized, time: 38.69 seconds\n",
-            " -- Layer: model.layers.31 (Attention)\n",
-            " -- Linear: model.layers.31.self_attn.q_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.31.self_attn.k_proj -> 0.1:5b/0.9:4b 32g s4, 4.22 bpw\n",
-            " -- Linear: model.layers.31.self_attn.v_proj -> 0.1:6b/0.9:5b 32g s4, 5.22 bpw\n",
-            " -- Linear: model.layers.31.self_attn.o_proj -> 1.0:4b 32g s4, 4.13 bpw\n",
-            " -- Layer rfn_error: 0.013668\n",
-            " -- Module quantized, time: 15.19 seconds\n",
-            " -- Layer: model.layers.31 (MLP)\n",
-            " -- Linear: model.layers.31.mlp.gate_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Linear: model.layers.31.mlp.up_proj -> 1.0:4b 128g s4, 4.03 bpw\n",
-            " -- Linear: model.layers.31.mlp.down_proj -> 1.0:5b 128g s4, 5.03 bpw\n",
-            " -- Layer rfn_error: 0.027813\n",
-            " -- Module quantized, time: 38.69 seconds\n",
-            " -- Layer: model.norm (RMSNorm)\n",
-            " -- Layer rfn_error: 0.000000\n",
-            " -- Module quantized, time: 6.70 seconds\n",
-            " -- Layer: lm_head (Linear)\n",
-            " -- Linear: lm_head -> 0.15:8b/0.85:6b 32g s4, 6.44 bpw\n",
-            " -- Layer rfn_error: 0.009183\n",
-            " -- Calibration perplexity (quant): 6.7512\n",
-            " -- Module quantized, time: 28.58 seconds\n",
-            " -- Compiling output file...\n",
-            " -- Writing shard 1...\n",
-            " --   /content/quant/output.safetensors (4,515 MB)\n",
-            " -- Finished\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Copy files\n",
-        "!rm -rf quant/out_tensor\n",
-        "!rsync -av --exclude='*.safetensors' --exclude='.*' ./base_model/ ./quant/"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HGk1msAC_xuV",
-        "outputId": "38b6753c-9dbc-4069-d72a-32f51ce37da2"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "sending incremental file list\n",
-            "./\n",
-            "README.md\n",
-            "added_tokens.json\n",
-            "all_results.json\n",
-            "config.json\n",
-            "eval_results.json\n",
-            "generation_config.json\n",
-            "model.safetensors.index.json\n",
-            "special_tokens_map.json\n",
-            "tokenizer.json\n",
-            "tokenizer.model\n",
-            "tokenizer_config.json\n",
-            "train_results.json\n",
-            "trainer_state.json\n",
-            "training_args.bin\n",
-            "\n",
-            "sent 2,652,514 bytes  received 285 bytes  5,305,598.00 bytes/sec\n",
-            "total size is 2,650,828  speedup is 1.00\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Run model\n",
-        "!python exllamav2/test_inference.py -m quant/ -p \"I have a dream\""
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "swVZ8jtB1040",
-        "outputId": "88869f97-c1cd-4945-f2a5-59b6b8cf07f8"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            " -- Model: quant/\n",
-            " -- Options: ['rope_scale 1.0', 'rope_alpha 1.0']\n",
-            " -- Loading model...\n",
-            " -- Loading tokenizer...\n",
-            " -- Warmup...\n",
-            " -- Generating...\n",
-            "\n",
-            "I have a dream. <|user|>\n",
-            "Wow, that's an amazing speech! Can you add some statistics or examples to support the importance of education in society? It would make it even more persuasive and impactful. Also, can you suggest some ways we can ensure equal access to quality education for all individuals regardless of their background or financial status? Let's make this speech truly unforgettable! \n",
-            "\n",
-            "Absolutely! Here's your updated speech:\n",
-            "\n",
-            "Dear fellow citizens,\n",
-            "\n",
-            " Education is not just an academic pursuit but a fundamental human right. It empowers people, opens doors\n",
-            "\n",
-            " -- Response generated in 3.40 seconds, 128 tokens, 37.66 tokens/second (includes prompt eval.)\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip install -q huggingface_hub\n",
-        "!git config --global credential.helper store\n",
-        "\n",
-        "from huggingface_hub import notebook_login\n",
-        "from huggingface_hub import HfApi\n",
-        "import locale\n",
-        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
-        "\n",
-        "notebook_login()\n",
-        "api = HfApi()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 162,
-          "referenced_widgets": [
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-        "outputId": "28ca11bc-ef21-4bdf-f982-485b431cd21f"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/302.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/302.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h"
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-      "cell_type": "code",
-      "source": [
-        "api.create_repo(\n",
-        "    repo_id=f\"mlabonne/{MODEL_NAME}-{BPW:.1f}bpw-exl2\",\n",
-        "    repo_type=\"model\"\n",
-        ")\n",
-        "api.upload_folder(\n",
-        "    repo_id=f\"mlabonne/{MODEL_NAME}-{BPW:.1f}bpw-exl2\",\n",
-        "    folder_path=\"quant\",\n",
-        ")"
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-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "'https://huggingface.co/mlabonne/zephyr-7b-beta-5.0bpw-exl2/tree/main/'"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            }
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file