模型簡介

DeepSeek-V3是DeepSeek團隊開發的新一代專家混合（MoE）語言模型，共有671B參數，在14.8萬億個Tokens上進行預訓練。該模型采用多頭潛在注意力（MLA）和DeepSeekMoE架構，繼承了DeepSeek-V2模型的優勢，并在性能、效率和功能上進行了顯著提升。

使用場景

DeepSeek-V3模型適用于多種自然語言處理任務，如文本生成、問答系統、文本摘要等，能夠生成高質量的語言內容并支持多語言對話。此外，它在數學推理、代碼生成等復雜任務中表現出色，可廣泛應用于教育、商業決策和編程輔助等領域。

評測效果

基礎模型評估

聊天模型評估

技術亮點

創新的負載均衡策略和訓練目標

• 除了DeepSeek-V2的高效架構之外，DeepSeek-V3開創了一種用于負載均衡的輔助無損策略，該策略可以最大限度地減少因鼓勵負載均衡而引起的性能下降。

• 多標記預測（MTP）目標，并證明它對模型性能有益，可用于推理加速的推測解碼。

邁向終極訓練效率

通過算法、框架和硬件的協同設計，克服了跨節點MoE訓練中的通信瓶頸，幾乎實現了完全的計算-通信重疊。顯著提高訓練效率并降低了訓練成本。

DeepSeek-R1的知識提煉

引入了一種創新方法，將長鏈思維（CoT）模型的推理能力，特別是DeepSeek R1系列模型之一的推理能力、驗證和反射模式整合到DeepSeek-V3，顯著提高了它的推理性能。

版本列表

相關資源及引用

相關資源

• 使用DeepSeek-V3 Base/Chat模型需遵守。

• DeepSeek-V3系列（包括Base和Chat）支持商用。

相關引用

@misc{deepseekai2024deepseekv3technicalreport,
      title={DeepSeek-V3 Technical Report}, 
      author={DeepSeek-AI and Aixin Liu and Bei Feng and Bing Xue and Bingxuan Wang and Bochao Wu and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Daya Guo and Dejian Yang and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J. L. Cai and Jian Liang and Jianzhong Guo and Jiaqi Ni and Jiashi Li and Jiawei Wang and Jin Chen and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and Junxiao Song and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Lei Xu and Leyi Xia and Liang Zhao and Litong Wang and Liyue Zhang and Meng Li and Miaojun Wang and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Mingming Li and Ning Tian and Panpan Huang and Peiyi Wang and Peng Zhang and Qiancheng Wang and Qihao Zhu and Qinyu Chen and Qiushi Du and R. J. Chen and R. L. Jin and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and Runxin Xu and Ruoyu Zhang and Ruyi Chen and S. S. Li and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shaoqing Wu and Shengfeng Ye and Shengfeng Ye and Shirong Ma and Shiyu Wang and Shuang Zhou and Shuiping Yu and Shunfeng Zhou and Shuting Pan and T. Wang and Tao Yun and Tian Pei and Tianyu Sun and W. L. Xiao and Wangding Zeng and Wanjia Zhao and Wei An and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and X. Q. Li and Xiangyue Jin and Xianzu Wang and Xiao Bi and Xiaodong Liu and Xiaohan Wang and Xiaojin Shen and Xiaokang Chen and Xiaokang Zhang and Xiaosha Chen and Xiaotao Nie and Xiaowen Sun and Xiaoxiang Wang and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xingkai Yu and Xinnan Song and Xinxia Shan and Xinyi Zhou and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and Y. K. Li and Y. Q. Wang and Y. X. Wei and Y. X. Zhu and Yang Zhang and Yanhong Xu and Yanhong Xu and Yanping Huang and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Li and Yaohui Wang and Yi Yu and Yi Zheng and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Ying Tang and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yu Wu and Yuan Ou and Yuchen Zhu and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yukun Zha and Yunfan Xiong and Yunxian Ma and Yuting Yan and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Z. F. Wu and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhen Huang and Zhen Zhang and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhibin Gou and Zhicheng Ma and Zhigang Yan and Zhihong Shao and Zhipeng Xu and Zhiyu Wu and Zhongyu Zhang and Zhuoshu Li and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Ziyi Gao and Zizheng Pan},
      year={2024},
      eprint={2412.19437},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={//arxiv.org/abs/2412.19437}, 
}

免責聲明

DeepSeek-V3模型來源于第三方，本平臺不保證其合規性，請您在使用前慎重考慮，確保合法合規使用并遵守第三方的要求。