Compressing large language models by joint sparsification and quantization
In this paper, we introduce a novel model compression technique named Joint Sparsification
and Quantization (JSQ), explicitly tailored for large language models (LLMs). Traditional …
and Quantization (JSQ), explicitly tailored for large language models (LLMs). Traditional …
Ptsbench: A comprehensive post-training sparsity benchmark towards algorithms and models
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 …
more and more prevalent because of its effectiveness and efficiency. However, there remain …
VRDistill: Vote Refinement Distillation for Efficient Indoor 3D Object Detection
Recently, indoor 3D object detection has shown impressive progress. However, these
improvements have come at the cost of increased memory consumption and longer …
improvements have come at the cost of increased memory consumption and longer …
QVD: Post-training Quantization for Video Diffusion Models
Recently, video diffusion models (VDMs) have garnered significant attention due to their
notable advancements in generating coherent and realistic video content. However …
notable advancements in generating coherent and realistic video content. However …
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment
Although large language models (LLMs) have demonstrated their strong intelligence ability,
the high demand for computation and storage hinders their practical application. To this end …
the high demand for computation and storage hinders their practical application. To this end …
On Efficient Variants of Segment Anything Model: A Survey
The Segment Anything Model (SAM) is a foundational model for image segmentation tasks,
known for its strong generalization across diverse applications. However, its impressive …
known for its strong generalization across diverse applications. However, its impressive …
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare
The disparity in healthcare personnel expertise and medical resources across different
regions of the world is a pressing social issue. Artificial intelligence technology offers new …
regions of the world is a pressing social issue. Artificial intelligence technology offers new …
BiDM: Pushing the Limit of Quantization for Diffusion Models
X Zheng, X Liu, Y Bian, X Ma, Y Zhang, J Wang… - The Thirty-eighth Annual … - openreview.net
Diffusion models (DMs) have been significantly developed and widely used in various
applications due to their excellent generative qualities. However, the expensive computation …
applications due to their excellent generative qualities. However, the expensive computation …