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

3fa5141

1 Parent(s): b23ee9c

Update README.md

Browse files

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README.md CHANGED Viewed

@@ -31,11 +31,15 @@ pip install protobuf
 ## Model Usage
 '''
 "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."
 '''
 where you need to use your query question to replace the {instruction}
 There are another interesting repo about Arithmo-Mistral-7B in https://huggingface.co/akjindal53244/Arithmo-Mistral-7B, where they combine our MetaMathQA dataset and MathInstruct datasets to train a powerful model. Thanks agian for their contributions.

 ## Model Usage
+prompting template:
 '''
 "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."
 '''
 where you need to use your query question to replace the {instruction}
 There are another interesting repo about Arithmo-Mistral-7B in https://huggingface.co/akjindal53244/Arithmo-Mistral-7B, where they combine our MetaMathQA dataset and MathInstruct datasets to train a powerful model. Thanks agian for their contributions.