Instructions to use nvidia/OpenMath-Mistral-7B-v0.1-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/OpenMath-Mistral-7B-v0.1-hf with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/OpenMath-Mistral-7B-v0.1-hf")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/OpenMath-Mistral-7B-v0.1-hf")
model = AutoModelForCausalLM.from_pretrained("nvidia/OpenMath-Mistral-7B-v0.1-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/OpenMath-Mistral-7B-v0.1-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/OpenMath-Mistral-7B-v0.1-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/OpenMath-Mistral-7B-v0.1-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nvidia/OpenMath-Mistral-7B-v0.1-hf

SGLang

How to use nvidia/OpenMath-Mistral-7B-v0.1-hf 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 "nvidia/OpenMath-Mistral-7B-v0.1-hf" \
    --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": "nvidia/OpenMath-Mistral-7B-v0.1-hf",
		"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 "nvidia/OpenMath-Mistral-7B-v0.1-hf" \
        --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": "nvidia/OpenMath-Mistral-7B-v0.1-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nvidia/OpenMath-Mistral-7B-v0.1-hf with Docker Model Runner:
```
docker model run hf.co/nvidia/OpenMath-Mistral-7B-v0.1-hf
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Mar 4, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+122

-6

leaderboard-pr-bot

Mar 4, 2024

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

Adding Evaluation Results381651cb

igitman

NVIDIA org Mar 5, 2024

Thanks for adding the evaluation results, but it looks like you might not have run the model correctly. Note that this model expects the code between special <llm-code>...</llm-code> tokens to be executed by Python interpreter and if that's not done, it will happily hallucinate outputs leading to very poor results (e.g. gsm8k score in this PR).

Please refer to https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md for how to run inference with our models.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

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This branch is ready to get merged automatically.

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