title: Qwen2.5-Coder Technical Report
summary: 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.
paper_authors._id: 66eb93a8772eb73ab0372cb9
paper_authors.name: Binyuan Hui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-19T07:06:39.282Z
paper_authors._id: 61e4c4ca1ab24785ac11ba69
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61e4c4ca1ab24785ac11ba69/1Q1zhhyGSJ9RJG9MzwxVv.jpeg
paper_authors.fullname: Binyuan Hui
paper_authors.type: user
paper_authors.user: huybery
paper_authors._id: 66eb93a8772eb73ab0372cba
paper_authors.name: Jian Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T08:07:52.138Z
paper_authors._id: 668642801af627989fda2b91
paper_authors.avatarUrl: /avatars/c8a880944529124c796ba51b9815148a.svg
paper_authors.fullname: yangjian
paper_authors.type: user
paper_authors.user: yangjian076
paper_authors._id: 66eb93a8772eb73ab0372cbb
paper_authors.name: Zeyu Cui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-23T14:22:35.249Z
paper_authors._id: 643fb14a18afbc4d1f3ebfb4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/643fb14a18afbc4d1f3ebfb4/2qeL_qPSB9_MTzZf46ynJ.png
paper_authors.fullname: czy yente
paper_authors.type: user
paper_authors.user: cyente
paper_authors._id: 66eb93a8772eb73ab0372cbc
paper_authors.name: Jiaxi Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T08:08:39.224Z
paper_authors._id: 6697a4b1a9e7257fc39d087a
paper_authors.avatarUrl: /avatars/65c53ede2f33075528cb3cc91ea743d1.svg
paper_authors.fullname: Jiaxi Yang
paper_authors.type: user
paper_authors.user: AbbottYJX
paper_authors._id: 66eb93a8772eb73ab0372cbd
paper_authors.name: Dayiheng Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T07:39:17.113Z
paper_authors._id: 6434d4989bd5a84b5dd0b0f5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6434d4989bd5a84b5dd0b0f5/0Elf9qbfG9Hkgypm9pTGm.jpeg
paper_authors.fullname: Dayiheng Liu
paper_authors.type: user
paper_authors.user: Losin94
paper_authors._id: 66eb93a8772eb73ab0372cbe
paper_authors.name: Lei Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-19T07:06:37.472Z
paper_authors._id: 64c38871f9cd765462fa1a17
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c38871f9cd765462fa1a17/yuIlVcqeDlQVKsUF8uEl3.jpeg
paper_authors.fullname: Lei Zhang
paper_authors.type: user
paper_authors.user: Lemoncoke
paper_authors._id: 66eb93a8772eb73ab0372cbf
paper_authors.name: Tianyu Liu
paper_authors._id: 66eb93a8772eb73ab0372cc0
paper_authors.name: Jiajun Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T08:09:12.743Z
paper_authors._id: 63f108072f7c0152e86fa2ea
paper_authors.avatarUrl: /avatars/992ec3f9642df1688c5b20b3f6c938dd.svg
paper_authors.fullname: Jiajun Zhang
paper_authors.type: user
paper_authors.user: jjzhang
paper_authors._id: 66eb93a8772eb73ab0372cc1
paper_authors.name: Bowen Yu
paper_authors._id: 66eb93a8772eb73ab0372cc2
paper_authors.name: Kai Dang
paper_authors._id: 66eb93a8772eb73ab0372cc3
paper_authors.name: An Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-23T16:29:42.378Z
paper_authors._id: 62088594a5943c8a8fc94560
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1644733028938-62088594a5943c8a8fc94560.png
paper_authors.fullname: An Yang
paper_authors.type: user
paper_authors.user: yangapku
paper_authors._id: 66eb93a8772eb73ab0372cc4
paper_authors.name: Rui Men
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-23T16:29:45.616Z
paper_authors._id: 6209bb200436d7d6f27cbeea
paper_authors.avatarUrl: /avatars/0b8a72a8b66ef7b36780fe2ccc343f78.svg
paper_authors.fullname: Iurnem
paper_authors.type: user
paper_authors.user: Iurnem
paper_authors._id: 66eb93a8772eb73ab0372cc5
paper_authors.name: Fei Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T08:10:52.587Z
paper_authors._id: 635b8b6a37c6a2c12e2cce00
paper_authors.avatarUrl: /avatars/229fb72180529141515d1df797b33709.svg
paper_authors.fullname: Fei Huang
paper_authors.type: user
paper_authors.user: hzhwcmhf
paper_authors._id: 66eb93a8772eb73ab0372cc6
paper_authors.name: Xingzhang Ren
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T08:11:16.807Z
paper_authors._id: 645c82c4081be4b32049633a
paper_authors.avatarUrl: /avatars/e5a08cf0ec5a04bd9d66111382ce0508.svg
paper_authors.fullname: xzhren
paper_authors.type: user
paper_authors.user: xingzhang
paper_authors._id: 66eb93a8772eb73ab0372cc7
paper_authors.name: Xuancheng Ren
paper_authors._id: 66eb93a8772eb73ab0372cc8
paper_authors.name: Jingren Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T07:39:33.037Z
paper_authors._id: 602f88f5e8149a962412a667
paper_authors.avatarUrl: /avatars/b78f0e583df8e5d5e3365934fe5f4900.svg
paper_authors.fullname: Zhou
paper_authors.type: user
paper_authors.user: Jingren
paper_authors._id: 66eb93a8772eb73ab0372cc9
paper_authors.name: Junyang Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-19T07:39:23.673Z
paper_authors._id: 620760a26e3b7210c2ff1943
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/620760a26e3b7210c2ff1943/VC-rKqimF6yxGESNVlPoR.jpeg
paper_authors.fullname: Junyang Lin
paper_authors.type: user
paper_authors.user: JustinLin610
paper_title: Qwen2.5-Coder Technical Report
paper_summary: 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.
paper_ai_summary: Qwen2.5-Coder series demonstrates state-of-the-art code generation, completion, reasoning, and repair capabilities using the Qwen2.5 architecture with over 5.5 trillion tokens of training data.
paper_ai_keywords: Qwen2.5-Coder Qwen2.5-Coder-1.5B Qwen2.5-Coder-7B Qwen2.5 architecture code generation code completion code reasoning code repair state-of-the-art SOTA