Instructions to use nvidia/OpenMath-CodeLlama-13b-Python-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/OpenMath-CodeLlama-13b-Python-hf with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/OpenMath-CodeLlama-13b-Python-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/OpenMath-CodeLlama-13b-Python-hf")
model = AutoModelForCausalLM.from_pretrained("nvidia/OpenMath-CodeLlama-13b-Python-hf")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nvidia/OpenMath-CodeLlama-13b-Python-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/OpenMath-CodeLlama-13b-Python-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-CodeLlama-13b-Python-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nvidia/OpenMath-CodeLlama-13b-Python-hf

SGLang

How to use nvidia/OpenMath-CodeLlama-13b-Python-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-CodeLlama-13b-Python-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-CodeLlama-13b-Python-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-CodeLlama-13b-Python-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-CodeLlama-13b-Python-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nvidia/OpenMath-CodeLlama-13b-Python-hf with Docker Model Runner:
```
docker model run hf.co/nvidia/OpenMath-CodeLlama-13b-Python-hf
```

igitman commited on Feb 16, 2024

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@@ -112,7 +112,7 @@ offering enterprises an easy, cost-effective, and fast way to adopt generative A
 If you find our work useful, please consider citing us!
 ```bibtex
-@article{toshnival2024openmath,
   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},

 If you find our work useful, please consider citing us!
 ```bibtex
+@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},