Qwen2.5-Coder Technical Report

Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Kai Dang, An Yang, Rui Men, Fei Huang, Xingzhang Ren, Xuancheng Ren, Jingren Zhou, Junyang Lin
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Abstract

In this report, we introduce the Qwen2.5-Coder series, a significant upgrade from its predecessor, CodeQwen1.5. This series includes two models: Qwen2.5-Coder-1.5B and Qwen2.5-Coder-7B. As a code-specific model, Qwen2.5-Coder is built upon the Qwen2.5 architecture and continues pretrained on a vast corpus of over 5.5 trillion tokens. Through meticulous data cleaning, scalable synthetic data generation, and balanced data mixing, Qwen2.5-Coder demonstrates impressive code generation capabilities while retaining general versatility. The model has been evaluated on a wide range of code-related tasks, achieving state-of-the-art (SOTA) performance across more than 10 benchmarks, including code generation, completion, reasoning, and repair, consistently outperforming larger models of the same model size. We believe that the release of the Qwen2.5-Coder series will not only push the boundaries of research in code intelligence but also, through its permissive licensing, encourage broader adoption by developers in real-world applications.
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Qwen2.5-Coder 技术报告
在本报告中,我们介绍了 Qwen2.5-Coder 系列,这是其前身 CodeQwen1.5 的重大升级。该系列包括两个型号:Qwen2.5-Coder-1.5B 和 Qwen2.5-Coder-7B。作为一个代码专用模型,Qwen2.5-Coder 建立在 Qwen2.5 架构之上,并在超过 5.5 万亿个 token 的庞大语料库中继续进行预训练。通过细致的数据清理、可扩展的合成数据生成和均衡的数据混合,Qwen2.5-Coder 展示了令人印象深刻的代码生成能力,同时保留了通用性。该模型已在广泛的代码相关任务中进行了评估,在代码生成、补全、推理和修复等 10 多个基准测试中取得了最先进(SOTA)的性能,其性能始终优于相同模型规模的大型模型。我们相信,Qwen2.5-Coder 系列的发布不仅将推动代码智能研究的发展,而且还将通过其许可授权,鼓励开发人员在实际应用中更广泛地采用它。
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