DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code
  Intelligence
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code
language model that achieves performance comparable to GPT4-Turbo in
code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained
from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion
tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially
enhances the coding and mathematical reasoning capabilities of DeepSeek-V2,
while maintaining comparable performance in general language tasks. Compared to
DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in
various aspects of code-related tasks, as well as reasoning and general
capabilities. Additionally, DeepSeek-Coder-V2 expands its support for
programming languages from 86 to 338, while extending the context length from
16K to 128K. In standard benchmark evaluations, DeepSeek-Coder-V2 achieves
superior performance compared to closed-source models such as GPT4-Turbo,
Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks.
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Qihao Zhu
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2024-06-19T08:26:08.420Z
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Zhihong Shao
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Dejian Yang
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DejianYang
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PEIYI, WANG
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Runxin Xu
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Y. Wu
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Hanwei Xu
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Damai Dai
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2024-06-19T08:28:06.857Z
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Damai Dai
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Kai Dong
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Zhibin Gou
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Zhenda Xie
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Junxiao Song
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Deli Chen
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deli chen
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Xuan Lu
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Qinyu Chen
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Yaohui Wang
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Chengqi Deng
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Jiashi Li
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Chong Ruan
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Chong Ruan
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Chester111
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Fuli Luo
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Wenfeng Liang
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code
  Intelligence
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code
language model that achieves performance comparable to GPT4-Turbo in
code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained
from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion
tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially
enhances the coding and mathematical reasoning capabilities of DeepSeek-V2,
while maintaining comparable performance in general language tasks. Compared to
DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in
various aspects of code-related tasks, as well as reasoning and general
capabilities. Additionally, DeepSeek-Coder-V2 expands its support for
programming languages from 86 to 338, while extending the context length from
16K to 128K. In standard benchmark evaluations, DeepSeek-Coder-V2 achieves
superior performance compared to closed-source models such as GPT4-Turbo,
Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks.
DeepSeek-Coder-V2, a Mixture-of-Experts language model, excels in code-specific tasks by enhancing coding and mathematical reasoning capabilities while expanding language support and context length.
Mixture-of-Experts DeepSeek-Coder DeepSeek-V2 coding tasks mathematical reasoning general language tasks programming languages context length coding benchmarks math benchmarks