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cs.CL2025
SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
Wenhua Cheng, Weiwei Zhang, Heng Guo +2
Extremely low-bit quantization is critical for efficiently deploying Large Language Models (LLMs), yet it often leads to severe performance degradation at 2 bits and even at 4 bits…
cs.CL2023★ 1 cited
TEQ: Trainable Equivalent Transformation for Quantization of LLMs
Wenhua Cheng, Yiyang Cai, Kaokao Lv +1
As large language models (LLMs) become more prevalent, there is a growing need for new and improved quantization methods that can meet the computationalast layer demands of these m…
cs.CL2023★ 1 cited
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
Wenhua Cheng, Weiwei Zhang, Haihao Shen +4
Large Language Models (LLMs) have demonstrated exceptional proficiency in language-related tasks, but their deployment poses significant challenges due to substantial memory and st…