3 papers
cs.LG2026
Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs
Yu Luo, Bo Dong, Wenhua Cheng +1
Serving large language models (LLMs) under diverse deployment constraints requires flexible trade-offs between accuracy, memory footprint, and throughput. However, conventional qua…
cs.CL2026
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.CL2024
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…