QuantFactory/wavecoder-ultra-6.7b-GGUF

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

QuantFactory/wavecoder-ultra-6.7b-GGUF

This is quantized version of 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/TeamCodeLLMAI"><b>[🐦 Twitter]</b></a> • <a href="https://www.reddit.com/r/LocalLLaMA/comments/19a1scy/wavecoderultra67bclaimstobethe2ndbestmodel/"><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!
• —[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.

🪁 Evaluation

Please refer to WaveCoder's GitHub repo for inference, evaluation, and training code.

# 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.

☕️ 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 when using the models and the datasets.