1 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference
Chao Zeng, Songwei Liu, Shu Yang +3
Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper presents Group Quantization and Sparse Acceleration (GQSA), a n…
cs.LG2024★ 1 cited
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Chao Zeng, Songwei Liu, Yusheng Xie +6
Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their practical application is constrained by substantial memory and computational dema…
cs.LG2024★ 1 cited
FoldGPT: Simple and Effective Large Language Model Compression Scheme
Songwei Liu, Chao Zeng, Lianqiang Li +4
The demand for deploying large language models(LLMs) on mobile devices continues to increase, driven by escalating data security concerns and cloud costs. However, network bandwidt…