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cs.LG2025
Tequila: Trapping-free Ternary Quantization for Large Language Models
Hong Huang, Decheng Wu, Rui Cen +7
Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often rely on mixed-precision multiplication t…
cs.LG2023★ 4 cited
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity
Yun Li, Lin Niu, Xipeng Zhang +3
Traditional pruning methods are known to be challenging to work in Large Language Models (LLMs) for Generative AI because of their unaffordable training process and large computati…