Instructions to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with Transformers:

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

pipe = pipeline("text-generation", model="WizardLMTeam/WizardCoder-Python-34B-V1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardCoder-Python-34B-V1.0")
model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardCoder-Python-34B-V1.0")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WizardLMTeam/WizardCoder-Python-34B-V1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WizardLMTeam/WizardCoder-Python-34B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WizardLMTeam/WizardCoder-Python-34B-V1.0

SGLang

How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 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 "WizardLMTeam/WizardCoder-Python-34B-V1.0" \
    --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": "WizardLMTeam/WizardCoder-Python-34B-V1.0",
		"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 "WizardLMTeam/WizardCoder-Python-34B-V1.0" \
        --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": "WizardLMTeam/WizardCoder-Python-34B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with Docker Model Runner:
```
docker model run hf.co/WizardLMTeam/WizardCoder-Python-34B-V1.0
```

WizardCoder-Python-34B-V1.0 / README.md

WizardLM

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	---
	license: llama2
	metrics:
	- code_eval
	library_name: transformers
	tags:
	- code
	model-index:
	- name: WizardCoder-Python-34B-V1.0
	results:
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.732
	verified: false
	---

	## WizardCoder: Empowering Code Large Language Models with Evol-Instruct

	<p style="font-size:28px;" align="center">
	🏠 <a href="https://wizardlm.github.io/" target="_blank">Home Page</a> </p>
	<p align="center">
	<p align="center">
	🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> </p>
	<p align="center">
	📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
	</p>
	<p align="center">
	👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
	</p>

	## News

	[2024/01/04] 🔥 We released WizardCoder-33B-V1.1 trained from deepseek-coder-33b-base, the SOTA OSS Code LLM on [EvalPlus Leaderboard](https://evalplus.github.io/leaderboard.html), achieves 79.9 pass@1 on HumanEval, 73.2 pass@1 on HumanEval-Plus, 78.9 pass@1 on MBPP, and 66.9 pass@1 on MBPP-Plus.

	[2024/01/04] 🔥 WizardCoder-33B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, and DeepSeek-Coder-33B-instruct on HumanEval and HumanEval-Plus pass@1.

	[2024/01/04] 🔥 WizardCoder-33B-V1.1 is comparable with ChatGPT 3.5, and surpasses Gemini Pro on MBPP and MBPP-Plus pass@1.

	\| Model \| Checkpoint \| Paper \| HumanEval \| HumanEval+ \| MBPP \| MBPP+ \| License \|
	\| ----- \|------\| ---- \|------\|-------\| ----- \| ----- \|----- \|
	\| GPT-4-Turbo (Nov 2023) \| - \| - \| 85.4 \| 81.7 \| 83.0 \| 70.7 \|-\|
	\| GPT-4 (May 2023) \| - \| - \| 88.4 \| 76.8 \| - \| - \|-\|
	\| GPT-3.5-Turbo (Nov 2023) \| - \| - \| 72.6 \| 65.9 \| 81.7 \| 69.4 \|-\|
	\| Gemini Pro \| - \| - \| 63.4 \| 55.5 \| 72.9 \| 57.9 \|-\|
	\| DeepSeek-Coder-33B-instruct \| - \| - \| 78.7 \| 72.6 \| 78.7 \| 66.7 \|-\|
	\| WizardCoder-33B-V1.1 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-33B-V1.1" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 79.9 \| 73.2 \| 78.9 \| 66.9 \| <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.1/resolve/main/LICENSE" target="_blank">MSFTResearch</a> \|
	\| WizardCoder-Python-34B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 73.2 \| 64.6 \| 73.2 \| 59.9 \| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> \|
	\| WizardCoder-15B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 59.8 \| 52.4 \| -- \| -- \| <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> \|
	\| WizardCoder-Python-13B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 64.0 \| -- \| -- \| -- \| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> \|
	\| WizardCoder-Python-7B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 55.5 \| -- \| -- \| -- \| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> \|
	\| WizardCoder-3B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 34.8 \| -- \| -- \| -- \| <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> \|
	\| WizardCoder-1B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 23.8 \| -- \| -- \| -- \| <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> \|



	- Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
	- Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is 24.8 points higher than the SOTA open-source LLM, and achieves 22.7 pass@1 on the [MATH Benchmarks](https://github.com/hendrycks/math), which is 9.2 points higher than the SOTA open-source LLM.

	<font size=4>

	\| Model \| Checkpoint \| Paper \| GSM8k \| MATH \|Online Demo\| License\|
	\| ----- \|------\| ---- \|------\|-------\| ----- \| ----- \|
	\| WizardMath-70B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 81.6 \| 22.7 \|[Demo](http://47.103.63.15:50083/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> \|
	\| WizardMath-13B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 63.9 \| 14.0 \|[Demo](http://47.103.63.15:50082/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> \|
	\| WizardMath-7B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 54.9 \| 10.7 \| [Demo ](http://47.103.63.15:50080/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>\|
	</font>


	- [08/09/2023] We released WizardLM-70B-V1.0 model. Here is [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-70B-V1.0).

	<font size=4>


	\| <sup>Model</sup> \| <sup>Checkpoint</sup> \| <sup>Paper</sup> \|<sup>MT-Bench</sup> \| <sup>AlpacaEval</sup> \| <sup>GSM8k</sup> \| <sup>HumanEval</sup> \| <sup>License</sup>\|
	\| ----- \|------\| ---- \|------\|-------\| ----- \| ----- \| ----- \|
	\| <sup>WizardLM-70B-V1.0</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-70B-V1.0" target="_blank">HF Link</a> </sup>\|<sup>📃Coming Soon</sup>\| <sup>7.78</sup> \| <sup>92.91%</sup> \|<sup>77.6%</sup> \| <sup> 50.6</sup>\|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> \|
	\| <sup>WizardLM-13B-V1.2</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>\| \| <sup>7.06</sup> \| <sup>89.17%</sup> \|<sup>55.3%</sup> \| <sup>36.6 </sup>\|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> \|
	\| <sup>WizardLM-13B-V1.1</sup> \|<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> \| \| <sup>6.76</sup> \|<sup>86.32%</sup> \| \| <sup>25.0 </sup>\| <sup>Non-commercial</sup>\|
	\| <sup>WizardLM-30B-V1.0</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> \| \| <sup>7.01</sup> \| \| \| <sup>37.8 </sup>\| <sup>Non-commercial</sup> \|
	\| <sup>WizardLM-13B-V1.0</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> \| \| <sup>6.35</sup> \| <sup>75.31%</sup> \| \| <sup> 24.0 </sup> \| <sup>Non-commercial</sup>\|
	\| <sup>WizardLM-7B-V1.0 </sup>\| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> \|<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>\| \| \| \|<sup>19.1 </sup>\|<sup> Non-commercial</sup>\|
	</font>


	## Comparing WizardCoder-Python-34B-V1.0 with Other LLMs.

	🔥 The following figure shows that our WizardCoder-Python-34B-V1.0 attains the second position in this benchmark, surpassing GPT4 (2023/03/15, 73.2 vs. 67.0), ChatGPT-3.5 (73.2 vs. 72.5) and Claude2 (73.2 vs. 71.2).

	<p align="center" width="100%">
	<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardCoder/imgs/compare_sota.png" alt="WizardCoder" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
	</p>

	## Prompt Format
	```
	"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
	```

	## Inference Demo Script

	We provide the inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).

	## Citation

	Please cite the repo if you use the data, method or code in this repo.

	```
	@article{luo2023wizardcoder,
	title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
	author={Luo, Ziyang and Xu, Can and Zhao, Pu and Sun, Qingfeng and Geng, Xiubo and Hu, Wenxiang and Tao, Chongyang and Ma, Jing and Lin, Qingwei and Jiang, Daxin},
	journal={arXiv preprint arXiv:2306.08568},
	year={2023}
	}
	```