title: WizardCoder: Empowering Code Large Language Models with Evol-Instruct
summary: Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated
  exceptional performance in code-related tasks. However, most existing models
  are solely pre-trained on extensive raw code data without instruction
  fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs
  with complex instruction fine-tuning, by adapting the Evol-Instruct method to
  the domain of code. Through comprehensive experiments on four prominent code
  generation benchmarks, namely HumanEval, HumanEval+, MBPP, and DS-1000, we
  unveil the exceptional capabilities of our model. It surpasses all other
  open-source Code LLMs by a substantial margin. Moreover, our model even
  outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on
  HumanEval and HumanEval+. Our code, model weights, and data are public at
  https://github.com/nlpxucan/WizardLM
paper_authors._id: 648bc6c9afaac0d4cb882625
paper_authors.name: Ziyang Luo
paper_authors.status: admin_assigned
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paper_authors.fullname: Ziyang Luo
paper_authors.type: user
paper_authors.user: Ziyang
paper_authors._id: 648bc6c9afaac0d4cb882626
paper_authors.name: Can Xu
paper_authors.status: admin_assigned
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paper_authors.fullname: WizardLM
paper_authors.type: user
paper_authors.user: nlpxucan
paper_authors._id: 648bc6c9afaac0d4cb882627
paper_authors.name: Pu Zhao
paper_authors._id: 648bc6c9afaac0d4cb882628
paper_authors.name: Qingfeng Sun
paper_authors.status: claimed_verified
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paper_authors.fullname: WizardLM
paper_authors.type: user
paper_authors.user: WizardLM
paper_authors._id: 648bc6c9afaac0d4cb882629
paper_authors.name: Xiubo Geng
paper_authors._id: 648bc6c9afaac0d4cb88262a
paper_authors.name: Wenxiang Hu
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paper_authors.fullname: Wenxiang Hu
paper_authors.type: user
paper_authors.user: wenxcs
paper_authors._id: 648bc6c9afaac0d4cb88262b
paper_authors.name: Chongyang Tao
paper_authors.status: claimed_verified
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paper_authors._id: 648db8f92eb481bc8bc60469
paper_authors.avatarUrl: /avatars/7a640980ed225e31117f750e09bcc18c.svg
paper_authors.fullname: Chongyang Tao
paper_authors.type: user
paper_authors.user: chongyang09
paper_authors._id: 648bc6c9afaac0d4cb88262c
paper_authors.name: Jing Ma
paper_authors._id: 648bc6c9afaac0d4cb88262d
paper_authors.name: Qingwei Lin
paper_authors._id: 648bc6c9afaac0d4cb88262e
paper_authors.name: Daxin Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-16T13:57:28.286Z
paper_authors._id: 63f087b39cf89c9ed1bb47fb
paper_authors.avatarUrl: /avatars/b1e0aae690f0ca0e2dbe9e0412c786ef.svg
paper_authors.fullname: Daxin Jiang
paper_authors.type: user
paper_authors.user: djiang3
paper_title: WizardCoder: Empowering Code Large Language Models with Evol-Instruct
paper_summary: Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated
  exceptional performance in code-related tasks. However, most existing models
  are solely pre-trained on extensive raw code data without instruction
  fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs
  with complex instruction fine-tuning, by adapting the Evol-Instruct method to
  the domain of code. Through comprehensive experiments on four prominent code
  generation benchmarks, namely HumanEval, HumanEval+, MBPP, and DS-1000, we
  unveil the exceptional capabilities of our model. It surpasses all other
  open-source Code LLMs by a substantial margin. Moreover, our model even
  outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on
  HumanEval and HumanEval+. Our code, model weights, and data are public at
  https://github.com/nlpxucan/WizardLM
paper_ai_summary: WizardCoder, a Code LLM fine-tuned with complex instructions using Evol-Instruct, outperforms other open-source and closed LLMs on several code generation benchmarks.
paper_ai_keywords: Code LLMs StarCoder WizardCoder Evol-Instruct HumanEval HumanEval+ MBPP DS-1000