29 citations · 59 across the 14 of their papers we have counts for
5 papers · 1 filter
DBellQuant: Breaking the Bell with Double-Bell Transformation for LLMs Post Training Binarization
Zijian Ye, Wei Huang, Yifei Yu +3
Large language models (LLMs) demonstrate remarkable performance but face substantial computational and memory challenges that limit their practical deployment. Quantization has eme…
Mixture Compressor for Mixture-of-Experts LLMs Gains More
Wei Huang, Yue Liao, Jianhui Liu +6
Mixture-of-Experts large language models (MoE-LLMs) marks a significant step forward of language models, however, they encounter two critical challenges in practice: 1) expert para…
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
Wei Huang, Haotong Qin, Yangdong Liu +7
Post-training quantization (PTQ) is an effective technique for compressing large language models (LLMs). However, while uniform-precision quantization is computationally efficient,…
An empirical study of LLaMA3 quantization: from LLMs to MLLMs
Wei Huang, Xingyu Zheng, Xudong Ma +7
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the…
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Wei Huang, Yangdong Liu, Haotong Qin +5
Pretrained large language models (LLMs) exhibit exceptional general language processing capabilities but come with significant demands on memory and computational resources. As a p…