6 citations · 19 across the 34 of their papers we have counts for
12 papers · 1 filter
PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models
Zining Wnag, Jinyang Guo, Ruihao Gong +5
With the increased attention to model efficiency, post-training sparsity (PTS) has become more and more prevalent because of its effectiveness and efficiency. However, there remain…
BiDM: Pushing the Limit of Quantization for Diffusion Models
Xingyu Zheng, Xianglong Liu, Yichen Bian +5
Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities. However, the expensive computation and…
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment
Ge Yang, Changyi He, Jinyang Guo +6
Although large language models (LLMs) have demonstrated their strong intelligence ability, the high demand for computation and storage hinders their practical application. To this…
SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models
Zonghao Ying, Aishan Liu, Siyuan Liang +5
Multimodal Large Language Models (MLLMs) are showing strong safety concerns (e.g., generating harmful outputs for users), which motivates the development of safety evaluation bench…
HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer Acceleration
Yushi Huang, Zining Wang, Ruihao Gong +5
Diffusion Transformers (DiTs) excel in generative tasks but face practical deployment challenges due to high inference costs. Feature caching, which stores and retrieves redundant…
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms
Ruihao Gong, Yifu Ding, Zining Wang +7
Large language models (LLMs) have achieved remarkable advancements in natural language processing, showcasing exceptional performance across various tasks. However, the expensive m…
DDK: Distilling Domain Knowledge for Efficient Large Language Models
Jiaheng Liu, Chenchen Zhang, Jinyang Guo +13
Despite the advanced intelligence abilities of large language models (LLMs) in various applications, they still face significant computational and storage demands. Knowledge Distil…
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…
Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes
Ruihao Gong, Yang Yong, Zining Wang +4
Neural network sparsity has attracted many research interests due to its similarity to biological schemes and high energy efficiency. However, existing methods depend on long-time…
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…
BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models
Xingyu Zheng, Xianglong Liu, Haotong Qin +7
With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and effici…
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…