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