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arXiv Machine Learning By Gongwei Lee, Ji Liu, Juncheng Jia, Ji Wu

FAMPWQ: Fisher Information-based Adaptive Mixed Precision Weight Quantization for Effective LLM Inference

Read the original on arXiv Machine Learning →

The paper introduces FAMPWQ, a Fisher information-based Adaptive Mixed Precision Weight Quantization method designed to improve Large Language Model inference on commodity GPUs. It uses a Fisher information metric to assess layer-wise sensitivity and a reinforcement learning-based bit-width allocator to adaptively assign precision per layer. Experiments across seven models and five benchmarks show significant gains in perplexity, accuracy, and LLM-as-a-judge performance compared to seven baseline approaches.

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