DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

• URL: http://arxiv.org/abs/2401.14196v2
• Date: Fri, 26 Jan 2024 09:23:11 GMT
• Title: DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
• Authors: Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y.K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang
• Abstract summary: We introduce the DeepSeek-Coder series, a range of open-source code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion tokens. Our evaluations demonstrate that DeepSeek-Coder achieves state-of-the-art performance among open-source code models across multiple benchmarks. DeepSeek-Coder models are under a permissive license that allows for both research and unrestricted commercial use.
• Score: 42.517055368627226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of large language models has revolutionized code intelligence in software development. However, the predominance of closed-source models has restricted extensive research and development. To address this, we introduce the DeepSeek-Coder series, a range of open-source code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion tokens. These models are pre-trained on a high-quality project-level code corpus and employ a fill-in-the-blank task with a 16K window to enhance code generation and infilling. Our extensive evaluations demonstrate that DeepSeek-Coder not only achieves state-of-the-art performance among open-source code models across multiple benchmarks but also surpasses existing closed-source models like Codex and GPT-3.5. Furthermore, DeepSeek-Coder models are under a permissive license that allows for both research and unrestricted commercial use.

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