Instructions to use meta-math/MetaMath-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-math/MetaMath-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="meta-math/MetaMath-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-math/MetaMath-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("meta-math/MetaMath-Mistral-7B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use meta-math/MetaMath-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-math/MetaMath-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-math/MetaMath-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/meta-math/MetaMath-Mistral-7B

SGLang

How to use meta-math/MetaMath-Mistral-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 "meta-math/MetaMath-Mistral-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": "meta-math/MetaMath-Mistral-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 "meta-math/MetaMath-Mistral-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": "meta-math/MetaMath-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use meta-math/MetaMath-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/meta-math/MetaMath-Mistral-7B
```

Longhui98 commited on Oct 22, 2023

Commit

4dc820d

1 Parent(s): cba988d

Update README.md

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@@ -3,7 +3,7 @@ license: llama2
 datasets:
 - meta-math/MetaMathQA
 ---
-arxiv.org/abs/2309.12284
 View the project page:
 https://meta-math.github.io/
@@ -87,7 +87,9 @@ We would also try to train the combination of **MetaMathQA** and **MathInstruct*
   journal={arXiv preprint arXiv:2309.12284},
   year={2023}
 }
 @article{jiang2023mistral,
   title={Mistral 7B},
   author={Jiang, Albert Q and Sablayrolles, Alexandre and Mensch, Arthur and Bamford, Chris and Chaplot, Devendra Singh and Casas, Diego de las and Bressand, Florian and Lengyel, Gianna and Lample, Guillaume and Saulnier, Lucile and others},

 datasets:
 - meta-math/MetaMathQA
 ---
+see our paper in https://arxiv.org/abs/2309.12284
 View the project page:
 https://meta-math.github.io/
   journal={arXiv preprint arXiv:2309.12284},
   year={2023}
 }
+```
+```bibtex
 @article{jiang2023mistral,
   title={Mistral 7B},
   author={Jiang, Albert Q and Sablayrolles, Alexandre and Mensch, Arthur and Bamford, Chris and Chaplot, Devendra Singh and Casas, Diego de las and Bressand, Florian and Lengyel, Gianna and Lample, Guillaume and Saulnier, Lucile and others},