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Hardware-Aligned and Natively Trainable Sparse Attention
The latest paper from DeepSeek introduces a new attention mechanism — NSA, a locally trainable sparse attention mechanism for ultra-fast long-context training and inference.
YiFan Zhang,Shanglin Lei,Runqi Qiao,Zhuoma GongQue,Xiaoshuai Song,Guanting Dong, Qiuna Tan, Zhe Wei, Peiqing Yang, Ye Tian, Yadong Xue, Xiaofei Wang,
CoRR (2024)
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Computing Research Repository (2024)
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Mixture of Block Attention for Long-Context LLMs
Kimi proposed a new attention mechanism, MoBA, which combines the principles of MoE and improves the efficiency of LLMs in long-text scenarios without sacrificing performance.
Minghao Xu, Lichuan Xiang,Xu Cai,Hongkai Wen
CoRR (2024)
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Benjamin Warner, Antoine Chaffin,Benjamin Clavié,Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas,Faisal Ladhak, Tom Aarsen,Nathan Cooper,Griffin Adams,
CoRR (2024)
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Frank F. Xu, Yufan Song, Boxuan Li, Yuxuan Tang, Kritanjali Jain, Mengxue Bao, Zora Z. Wang,Xuhui Zhou, Zhitong Guo, Murong Cao, Mingyang Yang, Hao Yang Lu,
Computing Research Repository (2024)
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