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

b23ee9c

1 Parent(s): 88c046c

Update README.md

Browse files

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README.md +1 -1

README.md CHANGED Viewed

@@ -68,7 +68,7 @@ We would also try to train the combination of **MetaMathQA** and **MathInstruct*
 | LLaMA-2-70B         | 56.8         | 13.5        |
 | WizardMath-13B      | 63.9         | 14.0        |
 | MAmmoTH-7B (COT)    | 50.5         | 10.4        |
-| MAmmoTH-7B (POT)    | 51.6         | 28.7        |
 | Arithmo-Mistral-7B  | 74.7         | 25.3        |
 | MetaMath-7B         | 66.5         | 19.8        |
 | MetaMath-13B        | 72.3         | 22.4        |

 | LLaMA-2-70B         | 56.8         | 13.5        |
 | WizardMath-13B      | 63.9         | 14.0        |
 | MAmmoTH-7B (COT)    | 50.5         | 10.4        |
+| MAmmoTH-7B (POT+COT)| 53.6         | 31.5        |
 | Arithmo-Mistral-7B  | 74.7         | 25.3        |
 | MetaMath-7B         | 66.5         | 19.8        |
 | MetaMath-13B        | 72.3         | 22.4        |