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--- |
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license: mit |
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license_link: https://huggingface.co/microsoft/wavecoder-ultra-6.7b/blob/main/LICENSE |
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language: |
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- en |
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library_name: transformers |
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datasets: |
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- humaneval |
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pipeline_tag: text-generation |
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tags: |
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- code |
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metrics: |
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- code_eval |
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--- |
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<h1 align="center"> |
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🌊 WaveCoder: Widespread And Versatile Enhanced Code LLM |
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</h1> |
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<p align="center"> |
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<a href="https://arxiv.org/abs/2312.14187"><b>[📜 Paper]</b></a> • |
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<!-- <a href=""><b>[🤗 HF Models]</b></a> • --> |
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<a href="https://github.com/microsoft/WaveCoder"><b>[🐱 GitHub]</b></a> |
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<br> |
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<a href="https://twitter.com/TeamCodeLLM_AI"><b>[🐦 Twitter]</b></a> • |
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<a href="https://www.reddit.com/r/LocalLLaMA/comments/19a1scy/wavecoderultra67b_claims_to_be_the_2nd_best_model/"><b>[💬 Reddit]</b></a> • |
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<a href="https://www.analyticsvidhya.com/blog/2024/01/microsofts-wavecoder-and-codeocean-revolutionize-instruction-tuning/">[🍀 Unofficial Blog]</a> |
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<!-- <a href="#-quick-start">Quick Start</a> • --> |
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<!-- <a href="#%EF%B8%8F-citation">Citation</a> --> |
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</p> |
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<p align="center"> |
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Repo for "<a href="https://arxiv.org/abs/2312.14187" target="_blank">WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation</a>" |
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</p> |
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## 🔥 News |
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- [2024/04/10] 🔥🔥🔥 WaveCoder repo, models released at [🤗 HuggingFace](https://huggingface.co/microsoft/wavecoder-ultra-6.7b)! |
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- [2023/12/26] WaveCoder paper released. |
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## 💡 Introduction |
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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. |
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| Model | HumanEval | MBPP(500) | HumanEval<br>Fix(Avg.) | HumanEval<br>Explain(Avg.) | |
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| -------------------------------------------------------------------------------- | --------- | --------- | ---------------------- | -------------------------- | |
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| GPT-4 | 85.4 | - | 47.8 | 52.1 | |
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| [🌊 WaveCoder-DS-6.7B](https://huggingface.co/microsoft/wavecoder-ds-6.7b) | 65.8 | 63.0 | 49.5 | 40.8 | |
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| [🌊 WaveCoder-Pro-6.7B](https://huggingface.co/microsoft/wavecoder-pro-6.7b) | 74.4 | 63.4 | 52.1 | 43.0 | |
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| [🌊 WaveCoder-Ultra-6.7B](https://huggingface.co/microsoft/wavecoder-ultra-6.7b) | 79.9 | 64.6 | 52.3 | 45.7 | |
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## 🪁 Evaluation |
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Please refer to WaveCoder's [GitHub repo](https://github.com/microsoft/WaveCoder) for inference, evaluation, and training code. |
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```python |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/wavecoder-ultra-6.7b") |
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model = AutoModelForCausalLM.from_pretrained("microsoft/wavecoder-ultra-6.7b") |
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``` |
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## 📖 License |
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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). |
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## ☕️ Citation |
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If you find this repository helpful, please consider citing our paper: |
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``` |
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@article{yu2023wavecoder, |
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title={Wavecoder: Widespread and versatile enhanced instruction tuning with refined data generation}, |
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author={Yu, Zhaojian and Zhang, Xin and Shang, Ning and Huang, Yangyu and Xu, Can and Zhao, Yishujie and Hu, Wenxiang and Yin, Qiufeng}, |
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journal={arXiv preprint arXiv:2312.14187}, |
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year={2023} |
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} |
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``` |
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## Note |
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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. |
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