WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Authors: name: Ziyang Luo, name: Can Xu, name: Pu Zhao, name: Qingfeng Sun, name: Xiubo Geng, name: Wenxiang Hu, name: Chongyang Tao, name: Jing Ma, name: Qingwei Lin, name: Daxin Jiang
Organization: Microsoft

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