29 citations · 30 across the 4 of their papers we have counts for
4 papers
QVD: Post-training Quantization for Video Diffusion Models
Shilong Tian, Hong Chen, Chengtao Lv +6
Recently, video diffusion models (VDMs) have garnered significant attention due to their notable advancements in generating coherent and realistic video content. However, processin…
PTQ4SAM: Post-Training Quantization for Segment Anything
Chengtao Lv, Hong Chen, Jinyang Guo +2
Segment Anything Model (SAM) has achieved impressive performance in many computer vision tasks. However, as a large-scale model, the immense memory and computation costs hinder its…
An empirical study of LLaMA3 quantization: from LLMs to MLLMs
Wei Huang, Xingyu Zheng, Xudong Ma +7
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the…
DB-LLM: Accurate Dual-Binarization for Efficient LLMs
Hong Chen, Chengtao Lv, Liang Ding +8
Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical dep…