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Qwen2.5-Coder Technical Report

About

In this report, we introduce the Qwen2.5-Coder series, a significant upgrade from its predecessor, CodeQwen1.5. This series includes six models: Qwen2.5-Coder-(0.5B/1.5B/3B/7B/14B/32B). 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 and math skills. These models have 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 advance research in code intelligence and, with its permissive licensing, support wider adoption by developers in real-world applications.

Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, Kai Dang, Yang Fan, Yichang Zhang, An Yang, Rui Men, Fei Huang, Bo Zheng, Yibo Miao, Shanghaoran Quan, Yunlong Feng, Xingzhang Ren, Xuancheng Ren, Jingren Zhou, Junyang Lin• 2024

Related benchmarks

TaskDatasetResultRank
Commonsense ReasoningHellaSwag
Accuracy80.5
1896
Commonsense ReasoningWinoGrande
Accuracy75.6
1442
Mathematical ReasoningGSM8K--
1398
Code GenerationHumanEval
Pass@192.7
1043
Question AnsweringARC Challenge
Accuracy64
906
Mathematical ReasoningMATH500 (test)--
895
Mathematical ReasoningMATH
Accuracy33.5
882
Language UnderstandingMMLU
Accuracy73.8
844
ReasoningBBH
Accuracy70.1
726
Physical Commonsense ReasoningPIQA
Accuracy48
696
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Other info

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