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