1.3k citations · 2.1k across the 15 of their papers we have counts for
15 papers
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Ji Lin, Jiaming Tang, Haotian Tang +7
Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce the cloud…
Stationary and moving bright solitons in Bose-Einstein condensates with spin-orbit coupling in a Zeeman field
JunTao He, Ji Lin
With the discovery of various matter wave solitons in spin-orbit-coupled Bose-Einstein condensates (BECs), exploring their properties has become increasingly significant. We mainly…
A Risk-Sensitive Global Maximum Principle for Controlled Fully Coupled FBSDEs with Applications
Jingtao Lin, Jingtao Shi
This paper is concerned with a kind of risk-sensitive optimal control problem for fully coupled forward-backward stochastic systems. The control variable enters the diffusion term…
Offsite-Tuning: Transfer Learning without Full Model
Guangxuan Xiao, Ji Lin, Song Han
Transfer learning is important for foundation models to adapt to downstream tasks. However, many foundation models are proprietary, so users must share their data with model owners…
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Guangxuan Xiao, Ji Lin, Mickael Seznec +3
Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods ca…
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
Muyang Li, Ji Lin, Chenlin Meng +3
During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of com…