Instructions to use meta-math/MetaMath-7B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-math/MetaMath-7B-V1.0 with Transformers:

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

pipe = pipeline("text-generation", model="meta-math/MetaMath-7B-V1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-math/MetaMath-7B-V1.0")
model = AutoModelForCausalLM.from_pretrained("meta-math/MetaMath-7B-V1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-math/MetaMath-7B-V1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-math/MetaMath-7B-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": "meta-math/MetaMath-7B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/meta-math/MetaMath-7B-V1.0

SGLang

How to use meta-math/MetaMath-7B-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 "meta-math/MetaMath-7B-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": "meta-math/MetaMath-7B-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 "meta-math/MetaMath-7B-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": "meta-math/MetaMath-7B-V1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use meta-math/MetaMath-7B-V1.0 with Docker Model Runner:
```
docker model run hf.co/meta-math/MetaMath-7B-V1.0
```

Longhui98 commited on Oct 11, 2023

Commit

51b1369

1 Parent(s): 9077898

Update README.md

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@@ -6,4 +6,16 @@ datasets:
 arxiv.org/abs/2309.12284
 View the project page:
-https://meta-math.github.io/

 arxiv.org/abs/2309.12284
 View the project page:
+https://meta-math.github.io/
+# Citation
+```bibtex
+@article{yu2023metamath,
+  title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
+  author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
+  journal={arXiv preprint arXiv:2309.12284},
+  year={2023}
+}
+```