Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent
by Xingwu Sun, Yanfeng Chen, Yiqing Huang, Ruobing Xie, Jiaqi Zhu, Kai Zhang, Shuaipeng Li, Zhen Yang, Jonny Han, Xiaobo Shu, Jiahao Bu, Zhongzhi Chen, Xuemeng Huang, Fengzong Lian, Saiyong Yang, Jianfeng Yan, Yuyuan Zeng, Xiaoqin Ren, Chao Yu, Lulu Wu, Yue Mao, Jun Xia, Tao Yang, Suncong Zheng, Kan Wu, Dian Jiao, Jinbao Xue, Xipeng Zhang, Decheng Wu, Kai Liu, Dengpeng Wu, Guanghui Xu, Shaohua Chen, Shuang Chen, Xiao Feng, Yigeng Hong, Junqiang Zheng, Chengcheng Xu, Zongwei Li, Xiong Kuang, Jianglu Hu, Yiqi Chen, Yuchi Deng, Guiyang Li, Ao Liu, Chenchen Zhang, Shihui Hu, Zilong Zhao, Zifan Wu, Yao Ding, Weichao Wang, Han Liu, Roberts Wang, Hao Fei, Peijie Yu, Ze Zhao, Xun Cao, Hai Wang, Fusheng Xiang, Mengyuan Huang, Zhiyuan Xiong, Bin Hu, Xuebin Hou, Lei Jiang, Jianqiang Ma, Jiajia Wu, Yaping Deng, Yi Shen, Qian Wang, Weijie Liu, Jie Liu, Meng Chen, Liang Dong, Weiwen Jia, Hu Chen, Feifei Liu, Rui Yuan, Huilin Xu, Zhenxiang Yan, Tengfei Cao, Zhichao Hu, Xinhua Feng, Dong Du, Tinghao Yu, Yangyu Tao, Feng Zhang, Jianchen Zhu, Chengzhong Xu, Xirui Li, Chong Zha, Wen Ouyang, Yinben Xia, Xiang Li, Zekun He, Rongpeng Chen, Jiawei Song, Ruibin Chen, Fan Jiang, Chongqing Zhao, Bo Wang
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Hunyuan-Large, a Transformer-based mixture of experts model with 389 billion parameters, capable of handling up to 256K tokens. It outperforms LLama3.1-70B on various benchmarks, including language understanding, generation, logical reasoning, and mathematical problem-solving. The model’s success is attributed to large-scale synthetic data, a mixed expert routing strategy, key-value cache compression, and an expert-specific learning rate schedule. The paper also investigates scaling laws and learning rate schedules for mixture of experts models, providing insights for future development and optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a huge AI model called Hunyuan-Large that can understand and generate lots of text. It’s really good at doing tasks like understanding language, generating text, and solving math problems. The model is so big that it needs special ways to store and use its information. The researchers also looked into how the model learns and gets better over time. They made all the code and data available for others to use and improve upon. |
Keywords
* Artificial intelligence * Language understanding * Mixture of experts * Optimization * Scaling laws * Synthetic data * Transformer
