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

Libraries

How to use nvidia/OpenMath-Nemotron-7B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/OpenMath-Nemotron-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/OpenMath-Nemotron-7B")
model = AutoModelForCausalLM.from_pretrained("nvidia/OpenMath-Nemotron-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nvidia/OpenMath-Nemotron-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/OpenMath-Nemotron-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/OpenMath-Nemotron-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/OpenMath-Nemotron-7B

SGLang

How to use nvidia/OpenMath-Nemotron-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 "nvidia/OpenMath-Nemotron-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/OpenMath-Nemotron-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Nemotron-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/OpenMath-Nemotron-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

igitman commited on Apr 30, 2025

Commit

ae96272

verified ·

1 Parent(s): 970acd3

Update README.md

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README.md +6 -6

README.md CHANGED Viewed

@@ -64,7 +64,7 @@ The pipeline we used to produce the data and models is fully open-sourced!
 We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/)
 to fully reproduce our results, including data generation.
-# How to use the models?
 Our models can be used in 3 inference modes: chain-of-thought (CoT), tool-integrated reasoning (TIR) and generative solution selection (GenSelect).
@@ -137,7 +137,7 @@ This model is intended to facilitate research in the area of mathematical reason
 Huggingface 04/23/2025 <br>
-## Model Architecture: <br>
 **Architecture Type:** Transformer decoder-only language model  <br>
@@ -148,7 +148,7 @@ Huggingface 04/23/2025 <br>
 ** This model has 1.5B of model parameters. <br>
-## Input: <br>
 **Input Type(s):** Text <br>
@@ -160,7 +160,7 @@ Huggingface 04/23/2025 <br>
-## Output: <br>
 **Output Type(s):** Text <br>
@@ -176,7 +176,7 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
-## Software Integration : <br>
 **Runtime Engine(s):** <br>
@@ -198,7 +198,7 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
-## Model Version(s):
 [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B)

 We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/)
 to fully reproduce our results, including data generation.
+## How to use the models?
 Our models can be used in 3 inference modes: chain-of-thought (CoT), tool-integrated reasoning (TIR) and generative solution selection (GenSelect).
 Huggingface 04/23/2025 <br>
+### Model Architecture: <br>
 **Architecture Type:** Transformer decoder-only language model  <br>
 ** This model has 1.5B of model parameters. <br>
+### Input: <br>
 **Input Type(s):** Text <br>
+### Output: <br>
 **Output Type(s):** Text <br>
+### Software Integration : <br>
 **Runtime Engine(s):** <br>
+### Model Version(s):
 [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B)