dMX is a differentiable mixed-precision framework that learns per-layer MXFP bit-width assignments for LLMs and outperforms KL-based heuristics on perplexity and zero-shot accuracy under bit-width budgets.
Q-ViT: Fully differentiable quantization for vision transformer.arXiv preprint arXiv:2201.07703, 2022
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dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats
dMX is a differentiable mixed-precision framework that learns per-layer MXFP bit-width assignments for LLMs and outperforms KL-based heuristics on perplexity and zero-shot accuracy under bit-width budgets.