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MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling
arXiv – CS AI| MiniCPM Team, Wenhao An, Yingfa Chen, Yewei Fang, Jiayi Li, Xin Li, Yaohui Li, Yishan Li, Yuxuan Li, Biyuan Lin, Chuan Liu, Hezi Liu, Siyuan Liu, Hongya Lyu, Yinxu Pan, Shixin Ren, Xingyu Shen, Zhou Su, Haojun Sun, Yangang Sun, Zhen Leng Thai, Xin Tian, Rui Wang, Xiaorong Wang, Yudong Wang, Bo Wu, Xiaoyue Xu, Dong Xu, Shuaikang Xue, Jiawei Yang, Bowen Zhang, Jinqian Zhang, Letian Zhang, Shengnan Zhang, Xinyu Zhang, Xinyuan Zhang, Zhu Zhang, Hengyu Zhao, Jiacheng Zhao, Zhi Zheng, Jie Zhou, Zihan Zhou, Shuo Wang, Chaojun Xiao, Xu Han, Zhiyuan Liu, Maosong Sun||2 views
🤖AI Summary
MiniCPM-SALA introduces a 9B-parameter hybrid language model architecture that combines sparse and linear attention mechanisms to handle ultra-long contexts up to 1M tokens. The model achieves 3.5x faster inference than full-attention models while reducing training costs by 75% through a continual training framework that transforms existing Transformer models.
Key Takeaways
- →MiniCPM-SALA uses a hybrid 1:3 ratio of sparse to linear attention mechanisms to balance performance and efficiency for long-context modeling.
- →The model supports context lengths up to 1M tokens on a single NVIDIA A6000D GPU where traditional 8B models fail due to memory constraints.
- →A cost-effective continual training framework reduces training costs by approximately 75% compared to training from scratch.
- →The architecture achieves up to 3.5x faster inference speed than full-attention models at 256K token sequences.
- →Extensive experiments show the hybrid model maintains general capabilities comparable to full-attention models while offering improved efficiency.
#minicpm-sala#hybrid-attention#long-context#llm#transformer#sparse-attention#linear-attention#inference-speed#memory-efficiency
Read Original →via arXiv – CS AI
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