---
license: mit
license_link: https://huggingface.co/microsoft/wavecoder-ultra-6.7b/blob/main/LICENSE
language:
  - en
library_name: transformers
datasets:
  - humaneval
pipeline_tag: text-generation
tags:
  - code
metrics:
  - code_eval
---

<h1 align="center">
🌊 WaveCoder: Widespread And Versatile Enhanced Code LLM
</h1>

<p align="center">
  <a href="https://arxiv.org/abs/2312.14187"><b>[📜 Paper]</b></a> •
  <!-- <a href=""><b>[🤗 HF Models]</b></a> • -->
  <a href="https://github.com/microsoft/WaveCoder"><b>[🐱 GitHub]</b></a>
  <br>
  <a href="https://twitter.com/TeamCodeLLM_AI"><b>[🐦 Twitter]</b></a> •
  <a href="https://www.reddit.com/r/LocalLLaMA/comments/19a1scy/wavecoderultra67b_claims_to_be_the_2nd_best_model/"><b>[💬 Reddit]</b></a> •
  <a href="https://www.analyticsvidhya.com/blog/2024/01/microsofts-wavecoder-and-codeocean-revolutionize-instruction-tuning/">[🍀 Unofficial Blog]</a>
  <!-- <a href="#-quick-start">Quick Start</a> • -->
  <!-- <a href="#%EF%B8%8F-citation">Citation</a> -->
</p>

<p align="center">
Repo for "<a href="https://arxiv.org/abs/2312.14187" target="_blank">WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation</a>" 
</p>

## 🔥 News

- [2024/04/10] 🔥🔥🔥 WaveCoder repo, models released at [🤗 HuggingFace](https://huggingface.co/microsoft/wavecoder-ultra-6.7b)!
- [2023/12/26] WaveCoder paper released.

## 💡 Introduction

WaveCoder 🌊 is a series of large language models (LLMs) for the coding domain, designed to solve relevant problems in the field of code through instruction-following learning. Its training dataset was generated from a subset of code-search-net data using a generator-discriminator framework based on LLMs that we proposed, covering four general code-related tasks: code generation, code summary, code translation, and code repair.

| Model                                                                            | HumanEval | MBPP(500) | HumanEval<br>Fix(Avg.) | HumanEval<br>Explain(Avg.) |
| -------------------------------------------------------------------------------- | --------- | --------- | ---------------------- | -------------------------- |
| GPT-4                                                                            | 85.4      | -         | 47.8                   | 52.1                       |
| [🌊 WaveCoder-DS-6.7B](https://huggingface.co/microsoft/wavecoder-ds-6.7b)       | 65.8      | 63.0      | 49.5                   | 40.8                       |
| [🌊 WaveCoder-Pro-6.7B](https://huggingface.co/microsoft/wavecoder-pro-6.7b)     | 74.4      | 63.4      | 52.1                   | 43.0                       |
| [🌊 WaveCoder-Ultra-6.7B](https://huggingface.co/microsoft/wavecoder-ultra-6.7b) | 79.9      | 64.6      | 52.3                   | 45.7                       |

## 🪁 Evaluation

Please refer to WaveCoder's [GitHub repo](https://github.com/microsoft/WaveCoder) for inference, evaluation, and training code.

```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/wavecoder-ultra-6.7b")
model = AutoModelForCausalLM.from_pretrained("microsoft/wavecoder-ultra-6.7b")
```

## 📖 License

This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the its [License](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL).

## ☕️ Citation

If you find this repository helpful, please consider citing our paper:

```
@article{yu2023wavecoder,
  title={Wavecoder: Widespread and versatile enhanced instruction tuning with refined data generation},
  author={Yu, Zhaojian and Zhang, Xin and Shang, Ning and Huang, Yangyu and Xu, Can and Zhao, Yishujie and Hu, Wenxiang and Yin, Qiufeng},
  journal={arXiv preprint arXiv:2312.14187},
  year={2023}
}
```

## Note

WaveCoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets.