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Title: Qwen2.5-Coder Technical Report

Authors: 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

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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