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cs.LG2025
DenoiseRotator: Enhance Pruning Robustness for LLMs via Importance Concentration
Tianteng Gu, Bei Liu, Bo Xiao +3
Pruning is a widely used technique to compress large language models (LLMs) by removing unimportant weights, but it often suffers from significant performance degradation - especia…
cs.LG2024★ 2 cited
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
Haoyu Wang, Bei Liu, Hang Shao +4
Parameter quantization for Large Language Models (LLMs) has attracted increasing attentions recently in reducing memory costs and improving computational efficiency. Early approach…