diff --git a/README.md b/README.md
index 765b1f9..c707556 100644
--- a/README.md
+++ b/README.md
@@ -39,20 +39,21 @@ A list of notebooks and articles related to large language models.
| Notebook | Description | Article | Notebook |
|---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Fine-tune Llama 2 with SFT | Step-by-step guide to supervised fine-tune Llama 2 in Google Colab. | [Article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) |
|
+| Fine-tune Llama 2 with QLoRA | Step-by-step guide to supervised fine-tune Llama 2 in Google Colab. | [Article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) |
|
| Fine-tune CodeLlama using Axolotl | End-to-end guide to the state-of-the-art tool for fine-tuning. | [Article](https://mlabonne.github.io/blog/posts/A_Beginners_Guide_to_LLM_Finetuning.html) |
|
-| Fine-tune Mistral-7b with SFT | Supervised fine-tune Mistral-7b in a free-tier Google Colab with TRL. | - |
|
+| Fine-tune Mistral-7b with QLoRA | Supervised fine-tune Mistral-7b in a free-tier Google Colab with TRL. | |
|
| Fine-tune Mistral-7b with DPO | Boost the performance of supervised fine-tuned models with DPO. | [Article](https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html) |
|
| Fine-tune Llama 3 with ORPO | Cheaper and faster fine-tuning in a single stage with ORPO. | [Article](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) |
|
+| Fine-tune Llama 3.1 with Unsloth | Ultra-efficient supervised fine-tuning in Google Colab. | [Article](https://mlabonne.github.io/blog/posts/2024-07-29_Finetune_Llama31.html) |
|
### Quantization
| Notebook | Description | Article | Notebook |
|---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|
-| 1. Introduction to Quantization | Large language model optimization using 8-bit quantization. | [Article](https://mlabonne.github.io/blog/posts/Introduction_to_Weight_Quantization.html) |
|
-| 2. 4-bit Quantization using GPTQ | Quantize your own open-source LLMs to run them on consumer hardware. | [Article](https://mlabonne.github.io/blog/4bit_quantization/) |
|
-| 3. Quantization with GGUF and llama.cpp | Quantize Llama 2 models with llama.cpp and upload GGUF versions to the HF Hub. | [Article](https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html) |
|
-| 4. ExLlamaV2: The Fastest Library to Run LLMs | Quantize and run EXL2 models and upload them to the HF Hub. | [Article](https://mlabonne.github.io/blog/posts/ExLlamaV2_The_Fastest_Library_to_Run%C2%A0LLMs.html) |
|
+| Introduction to Quantization | Large language model optimization using 8-bit quantization. | [Article](https://mlabonne.github.io/blog/posts/Introduction_to_Weight_Quantization.html) |
|
+| 4-bit Quantization using GPTQ | Quantize your own open-source LLMs to run them on consumer hardware. | [Article](https://mlabonne.github.io/blog/4bit_quantization/) |
|
+| Quantization with GGUF and llama.cpp | Quantize Llama 2 models with llama.cpp and upload GGUF versions to the HF Hub. | [Article](https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html) |
|
+| ExLlamaV2: The Fastest Library to Run LLMs | Quantize and run EXL2 models and upload them to the HF Hub. | [Article](https://mlabonne.github.io/blog/posts/ExLlamaV2_The_Fastest_Library_to_Run%C2%A0LLMs.html) |
|
### Other
@@ -66,7 +67,7 @@ A list of notebooks and articles related to large language models.
## 🧩 LLM Fundamentals
-This section introduces essential knowledge about mathematics, Python, and neural networks. You might not want to start here, but refer to it as needed.
+This section introduces essential knowledge about mathematics, Python, and neural networks. You might not want to start here but refer to it as needed.
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