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README.md

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+
+---
+
+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
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/wavecoder-ultra-6.7b-GGUF
+This is quantized version of [microsoft/wavecoder-ultra-6.7b](https://huggingface.co/microsoft/wavecoder-ultra-6.7b) created using llama.cpp
+
+# Original Model Card
+
+
+<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.
+