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JiaxinHu

1 accepted papers

2025

FlatQuant: Flatness Matters for LLM Quantization

ICML 2025poster

Recently, quantization has been widely used for the compression and acceleration of large language models (LLMs). Due to the outliers in LLMs, it is crucial to flatten weights and activations to minimize quantization error with equally spaced quantization points. Prior research explores various pre-…