cjwbw / magicoder

LLMs with open-source code snippets for generating low-bias and high-quality instruction data for code.

  • Public
  • 344 runs
  • GitHub
  • Paper
  • License
Run with an API
Playground API Examples README Versions

Input

Output

Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 79 seconds. The predict time for this model varies significantly based on the inputs.

Readme

🎩 Magicoder: Source Code Is All You Need

About

  • 🎩Magicoder is a model family empowered by 🪄OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets for generating low-bias and high-quality instruction data for code.
  • 🪄OSS-Instruct mitigates the inherent bias of the LLM-synthesized instruction data by empowering them with a wealth of open-source references to produce more diverse, realistic, and controllable data.

Overview of OSS-Instruct Overview of Result

📝 Citation

@article{wei2023magicoder,
  title={Magicoder: Source Code Is All You Need},
  author={Wei, Yuxiang and Wang, Zhe and Liu, Jiawei and Ding, Yifeng and Zhang, Lingming},
  journal={arXiv preprint arXiv:2312.02120},
  year={2023}
}

🙏 Acknowledgements

We thank AK(@_akhaliq) and the Hugging Face team for their support in the Magicoder Playground! We also thank the following amazing projects that truly inspired us:

⚠️ Important Note

  • Bias, Risks, and Limitations: Magicoders may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding.

  • Usage: Magicoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI’s terms of use when using the models and the datasets. Magicoders will not compete with any OpenAI’s commercial product.