9 citations · 14 across the 7 of their papers we have counts for
7 papers
CoSA: Accelerating Long-Context Inference via Proxy-Kernel Co-Designed Sparse Attention
Yufei Xue, Lin Niu, Hong Liu +6
The quadratic cost of self-attention makes long-context inference prohibitively expensive, and proxy-based block-sparse attention has become a practical remedy. Existing methods ty…
PIVOT: Efficient Query-Group Indexing for Token-Level Sparse Attention
Hong Liu, Yuan Cheng, Lin Niu +5
Token-level sparse attention, as implemented by DeepSeek Sparse Attention (DSA) in production systems, makes the downstream attention efficient but shifts the bottleneck to the ind…
AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compression
Rui Cen, QiangQiang Hu, Hong Huang +10
This technical report introduces AngelSlim, a comprehensive and versatile toolkit for large model compression developed by the Tencent Hunyuan team. By consolidating cutting-edge a…
Stem: Rethinking Causal Information Flow in Sparse Attention
Lin Niu, Xin Luo, Linchuan Xie +4
The quadratic computational complexity of self-attention remains a fundamental bottleneck for scaling Large Language Models (LLMs) to long contexts, particularly during the pre-fil…
Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
Team Hunyuan3D, Shuhui Yang, Mingxin Yang +50
3D AI-generated content (AIGC) is a passionate field that has significantly accelerated the creation of 3D models in gaming, film, and design. Despite the development of several gr…
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
Zibo Zhao, Zeqiang Lai, Qingxiang Lin +71
We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-sca…
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity
Yun Li, Lin Niu, Xipeng Zhang +3
Traditional pruning methods are known to be challenging to work in Large Language Models (LLMs) for Generative AI because of their unaffordable training process and large computati…