Instructions to use microsoft/wavecoder-ds-6.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/wavecoder-ds-6.7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="microsoft/wavecoder-ds-6.7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/wavecoder-ds-6.7b")
model = AutoModelForCausalLM.from_pretrained("microsoft/wavecoder-ds-6.7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/wavecoder-ds-6.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/wavecoder-ds-6.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/wavecoder-ds-6.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/microsoft/wavecoder-ds-6.7b

SGLang

How to use microsoft/wavecoder-ds-6.7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "microsoft/wavecoder-ds-6.7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/wavecoder-ds-6.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "microsoft/wavecoder-ds-6.7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/wavecoder-ds-6.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use microsoft/wavecoder-ds-6.7b with Docker Model Runner:
```
docker model run hf.co/microsoft/wavecoder-ds-6.7b
```

wavecoder-ds-6.7b / README.md

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	---
	license: other
	library_name: transformers
	datasets:
	- humaneval
	license_name: deepseek
	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.

	## ☕️ 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.