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cs.LG2026
EPTS: Elastic Post-Training Sparsity for Efficient Large Language Model Compression
Ke Xu, Jiaqi Wan, Wenhao Hu +2
Post-Training Sparsity (PTS) has emerged as a crucial paradigm for compressing Large Language Models to facilitate efficient deployment on resource-constrained devices. However, ex…
cs.LG2026
Rethinking Residual Errors in Compensation-based LLM Quantization
Shuaiting Li, Juncan Deng, Kedong Xu +5
Methods based on weight compensation, which iteratively apply quantization and weight compensation to minimize the output error, have recently demonstrated remarkable success in qu…
cs.LG2024
VQ4ALL: Efficient Neural Network Representation via a Universal Codebook
Juncan Deng, Shuaiting Li, Zeyu Wang +3
The rapid growth of the big neural network models puts forward new requirements for lightweight network representation methods. The traditional methods based on model compression h…