Instructions to use RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits")
model = AutoModelForMultimodalLM.from_pretrained("RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits

SGLang

How to use RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits 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 "RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits" \
    --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": "RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits",
		"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 "RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits" \
        --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": "RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

OpenMath-CodeLlama-7b-Python-hf - bnb 8bits

Model creator: https://huggingface.co/nvidia/
Original model: https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf/

Original model description:

license: llama2 base_model: - codellama/CodeLlama-7b-Python-hf datasets: - nvidia/OpenMathInstruct-1 language: - en tags: - nvidia - code - math

OpenMath-CodeLlama-7b-Python-hf

OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks executed by Python interpreter. The models were trained on OpenMathInstruct-1, a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed Mixtral-8x7B model.

	greedy		majority@50
model	GSM8K	MATH	GMS8K	MATH
OpenMath-CodeLlama-7B (nemo \| HF)	75.9	43.6	84.8	55.6
OpenMath-Mistral-7B (nemo \| HF)	80.2	44.5	86.9	57.2
OpenMath-CodeLlama-13B (nemo \| HF)	78.8	45.5	86.8	57.6
OpenMath-CodeLlama-34B (nemo \| HF)	80.7	48.3	88.0	60.2
OpenMath-Llama2-70B (nemo \| HF)	84.7	46.3	90.1	58.3
OpenMath-CodeLlama-70B (nemo \| HF)	84.6	50.7	90.8	60.4

The pipeline we used to produce these models is fully open-sourced!

See our paper for more details!

How to use the models?

Try to run inference with our models with just a few commands!

Reproducing our results

We provide all instructions to fully reproduce our results.

Improving other models

To improve other models or to learn more about our code, read through the docs below.

NeMo-Skills Pipeline

In our pipeline we use NVIDIA NeMo, an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI.

Citation

If you find our work useful, please consider citing us!

@article{toshniwal2024openmath,
  title   = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
  author  = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
  year    = {2024},
  journal = {arXiv preprint arXiv: Arxiv-2402.10176}
}

License

The use of this model is governed by the Llama 2 Community License Agreement

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Safetensors

Model size

7B params

Tensor type

F32

F16

Paper for RichardErkhov/nvidia_-_OpenMath-CodeLlama-7b-Python-hf-8bits

OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset

Paper • 2402.10176 • Published Feb 15, 2024 • 38