Instructions to use WizardLMTeam/WizardMath-70B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WizardLMTeam/WizardMath-70B-V1.0 with Transformers:

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

pipe = pipeline("text-generation", model="WizardLMTeam/WizardMath-70B-V1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardMath-70B-V1.0")
model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardMath-70B-V1.0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WizardLMTeam/WizardMath-70B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WizardLMTeam/WizardMath-70B-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/WizardMath-70B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WizardLMTeam/WizardMath-70B-V1.0

SGLang

How to use WizardLMTeam/WizardMath-70B-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/WizardMath-70B-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/WizardMath-70B-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/WizardMath-70B-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/WizardMath-70B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WizardLMTeam/WizardMath-70B-V1.0 with Docker Model Runner:
```
docker model run hf.co/WizardLMTeam/WizardMath-70B-V1.0
```

WizardMath-70B-V1.0 / README.md

WizardLM

Update README.md

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	---
	license: llama2
	---


	## WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct



	<p align="center">
	🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> <br>
	</p>
	<p align="center">
	👋 Join our <a href="https://discord.gg/bpmeZD7V" target="_blank">Discord</a>
	</p>








	\| 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> \| 📃Coming Soon\| 81.6 \| 22.7 \|\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a> \|
	\| WizardMath-13B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> \| 📃Coming Soon\| 63.9 \| 14.0 \| \| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a> \|
	\| WizardMath-7B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> \| 📃Coming Soon\| 54.9 \| 10.7 \| [Demo-1](http://777957f.r10.cpolar.top), [Demo-2](http://2be2671b.r10.cpolar.top) \| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a>\|


	Github Repo: https://github.com/nlpxucan/WizardLM/tree/main/WizardMath

	Twitter: https://twitter.com/WizardLM_AI/status/1689990201467432960

	Discord: https://discord.gg/bpmeZD7V



	❗<b>Note for model system prompts usage:</b>

	Please use the same systems prompts strictly with us, and we do not guarantee the accuracy of the quantified versions.

	Default version:

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


	CoT Version: （❗For the simple math questions, we do NOT recommend to use the CoT prompt.）


	```
	"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step."
	```


	❗<b>To commen concern about dataset:</b>

	Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models.
	Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team .
	Our researchers have no authority to publicly release them without authorization.
	Thank you for your understanding.