4 papers
Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification
Hong Huang, Decheng Wu, Qiangqiang Hu +5
The deployment of Large Language Models (LLMs) on resource-constrained edge devices is increasingly hindered by prohibitive memory and computational requirements. While ternary qua…
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…
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis
Hong Huang, Dapeng Wu
Large language models (LLMs) have made exciting achievements across various domains, yet their deployment on resource-constrained personal devices remains hindered by the prohibiti…
FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
Hong Huang, Hai Yang, Yuan Chen +2
Federated Learning (FL) enables collaborative model training across distributed clients without data sharing, but its high computational and communication demands strain resource-c…