17 citations · 26 across the 5 of their papers we have counts for
5 papers
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
Sifan Zhou, Liang Li, Xinyu Zhang +6
Due to highly constrained computing power and memory, deploying 3D lidar-based detectors on edge devices equipped in autonomous vehicles and robots poses a crucial challenge. Being…
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…
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Liang Li, Qingyuan Li, Bo Zhang +1
As the size of large language models (LLMs) continues to grow, model compression without sacrificing accuracy has become a crucial challenge for deployment. While some quantization…
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…
Make RepVGG Greater Again: A Quantization-aware Approach
Xiangxiang Chu, Liang Li, Bo Zhang
The tradeoff between performance and inference speed is critical for practical applications. Architecture reparameterization obtains better tradeoffs and it is becoming an increasi…