3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2023
A Speed Odyssey for Deployable Quantization of LLMs
Qingyuan Li, Ran Meng, Yiduo Li +6
The large language model era urges faster and less costly inference. Prior model compression works on LLMs tend to undertake a software-centric approach primarily focused on the si…
cs.CL2023★ 3 cited
FPTQ: Fine-grained Post-Training Quantization for Large Language Models
Qingyuan Li, Yifan Zhang, Liang Li +6
In the era of large-scale language models, the substantial parameter size poses significant challenges for deployment. Being a prevalent compression technique, quantization has eme…
cs.LG2023
Stochastic Ratios Tracking Algorithm for Large Scale Machine Learning Problems
Shigeng Sun, Yuchen Xie
Many machine learning applications and tasks rely on the stochastic gradient descent (SGD) algorithm and its variants. Effective step length selection is crucial for the success of…