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Rethinking Shrinkage Bias in LLM FP4 Pretraining: Geometric Origin, Systemic Impact, and UFP4 Recipe

Hugging Face Daily Papers · 6d ago Cached

This paper identifies a fundamental limitation (shrinkage bias) in non-uniform FP4 quantization formats for LLM pretraining and proposes UFP4, a uniform 4-bit training recipe that outperforms existing E2M1-based methods.

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#fp4

@Italianclownz: Converted Qwen 3.6 35b a3b to ROCmfp4 and this is flying. Used the mtp version bc this ROCmfp4 can also incorporate the…

X AI KOLs Timeline · 2026-05-24 Cached

Converted the Qwen 3.6 35b a3b model to ROCmfp4 format, leveraging MTP benefits for improved performance on AMD hardware.

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#fp4

@charles_irl: another page for the @modal LLMEng Almanac: an explorer for low-precision floats, from bf16 to fp4 https://modal.com/ll…

X AI KOLs Following · 2026-05-18 Cached

A page from Modal's LLM Engineer's Almanac that provides an interactive explorer for understanding low-precision floating-point formats like bf16 and fp4.

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