Point obliteratus at an FP8 or NVFP4 checkpoint and it just works:
the loader detects the format from config.json + safetensors metadata
(no weight loads), dequantizes shard-by-shard to a temporary BF16 copy,
then runs the normal float pipeline and saves BF16.
Supported layouts:
- FP8 DeepSeek-style block-wise (weight_scale_inv + weight_block_size)
- FP8 per-channel / per-tensor (compressed-tensors, ModelOpt)
- NVFP4 ModelOpt (uint8 nibbles + FP8 group scales + FP32 global),
including MIXED_PRECISION checkpoints (FP8 mixer + NVFP4 experts)
- NVFP4 compressed-tensors (reciprocal scales)
Design:
- New pure-torch obliteratus/models/quant_dequant.py; no new deps.
NVFP4 unpack uses torch.float4_e2m1fn_x2 when a runtime probe proves
it works, else a chunked nibble LUT (bounds transient int64 index
memory; a naive implementation OOMed at 96GB on a 30B model).
- Scale keys are dropped only when their base weight exists in the
same shard, so legitimate params ending in _scale (logit_scale et al.)
survive.
- Unsupported schemes (fbgemm, quanto, W4A4, ...) fail loudly at load,
naming the scheme.
- Surgery guards: float8 or packed uint8 reaching _dequantize_weight or
any fused-MoE path raises RuntimeError instead of silently upcasting
(bitsandbytes quant_state params are explicitly excluded).
- Save path strips quantization metadata and logs that output is BF16;
re-quantization for serving is out of scope (llm-compressor/modelopt).
- CLI: new --trust-remote-code flag; help text documents auto-detection.
Validated end-to-end on 1x A100-80GB (see PR description):
Nemotron-3-Nano-Omni-30B NVFP4 (mixed) and FP8, Qwen3-8B-FP8
(block-wise) vs Qwen3-8B BF16 baseline (perplexity 4.23 vs 4.33).
