Update README.md
Browse files
README.md
CHANGED
@@ -1,3 +1,68 @@
|
|
1 |
---
|
2 |
-
license:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
---
|
2 |
+
license: other
|
3 |
+
library_name: transformers
|
4 |
+
datasets:
|
5 |
+
- humaneval
|
6 |
+
license_name: deepseek
|
7 |
+
pipeline_tag: text-generation
|
8 |
+
tags:
|
9 |
+
- code
|
10 |
+
metrics:
|
11 |
+
- code_eval
|
12 |
---
|
13 |
+
<h1 align="center">
|
14 |
+
🌊 WaveCoder: Widespread And Versatile Enhanced Code LLM
|
15 |
+
</h1>
|
16 |
+
|
17 |
+
|
18 |
+
<p align="center">
|
19 |
+
<a href="https://arxiv.org/abs/2312.14187"><b>[📜 Paper]</b></a> •
|
20 |
+
<!-- <a href=""><b>[🤗 HF Models]</b></a> • -->
|
21 |
+
<a href="https://github.com/microsoft/WaveCoder"><b>[🐱 GitHub]</b></a>
|
22 |
+
<br>
|
23 |
+
<a href="https://twitter.com/TeamCodeLLM_AI"><b>[🐦 Twitter]</b></a> •
|
24 |
+
<a href="https://www.reddit.com/r/LocalLLaMA/comments/19a1scy/wavecoderultra67b_claims_to_be_the_2nd_best_model/"><b>[💬 Reddit]</b></a> •
|
25 |
+
<a href="https://www.analyticsvidhya.com/blog/2024/01/microsofts-wavecoder-and-codeocean-revolutionize-instruction-tuning/">[🍀 Unofficial Blog]</a>
|
26 |
+
<!-- <a href="#-quick-start">Quick Start</a> • -->
|
27 |
+
<!-- <a href="#%EF%B8%8F-citation">Citation</a> -->
|
28 |
+
</p>
|
29 |
+
|
30 |
+
<p align="center">
|
31 |
+
Repo for "<a href="https://arxiv.org/abs/2312.14187" target="_blank">WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation</a>"
|
32 |
+
</p>
|
33 |
+
|
34 |
+
|
35 |
+
## 🔥 News
|
36 |
+
|
37 |
+
- [2024/04/10] 🔥🔥🔥 WaveCoder repo, models released at [🤗 HuggingFace](https://huggingface.co/microsoft/wavecoder-ultra-6.7b)!
|
38 |
+
- [2023/12/26] WaveCoder paper released.
|
39 |
+
## 💡 Introduction
|
40 |
+
|
41 |
+
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.
|
42 |
+
|
43 |
+
| Model | HumanEval | MBPP(500) | HumanEval<br>Fix(Avg.) | HumanEval<br>Explain(Avg.)|
|
44 |
+
|---|---|---|---|---|
|
45 |
+
| GPT-4 | 85.4 | - | 47.8 | 52.1 |
|
46 |
+
| [🌊 WaveCoder-DS-6.7B](https://huggingface.co/microsoft/wavecoder-ds-6.7b) | 65.8 | 63.0 | 49.5 | 40.8|
|
47 |
+
| [🌊 WaveCoder-Pro-6.7B](https://huggingface.co/microsoft/wavecoder-pro-6.7b) | 74. 4 | 63.4 | 52.1 | 43.0 |
|
48 |
+
| [🌊 WaveCoder-Ultra-6.7B](https://huggingface.co/microsoft/wavecoder-ultra-6.7b) | 79.9 | 64.6 | 52.3 | 45.7 |
|
49 |
+
|
50 |
+
## 🪁 Evaluation
|
51 |
+
|
52 |
+
Please refer to WaveCoder's [GitHub repo](https://github.com/microsoft/WaveCoder) for inference, evaluation, and training code.
|
53 |
+
|
54 |
+
## ☕️ Citation
|
55 |
+
|
56 |
+
If you find this repository helpful, please consider citing our paper:
|
57 |
+
|
58 |
+
```
|
59 |
+
@article{yu2023wavecoder,
|
60 |
+
title={Wavecoder: Widespread and versatile enhanced instruction tuning with refined data generation},
|
61 |
+
author={Yu, Zhaojian and Zhang, Xin and Shang, Ning and Huang, Yangyu and Xu, Can and Zhao, Yishujie and Hu, Wenxiang and Yin, Qiufeng},
|
62 |
+
journal={arXiv preprint arXiv:2312.14187},
|
63 |
+
year={2023}
|
64 |
+
}
|
65 |
+
```
|
66 |
+
## Note
|
67 |
+
|
68 |
+
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.
|