Instructions to use nvidia/OpenMath-Nemotron-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/OpenMath-Nemotron-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/OpenMath-Nemotron-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/OpenMath-Nemotron-1.5B")
model = AutoModelForCausalLM.from_pretrained("nvidia/OpenMath-Nemotron-1.5B")
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 Settings

vLLM

How to use nvidia/OpenMath-Nemotron-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/OpenMath-Nemotron-1.5B"
# 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-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/OpenMath-Nemotron-1.5B

SGLang

How to use nvidia/OpenMath-Nemotron-1.5B 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-1.5B" \
    --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-1.5B",
		"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-1.5B" \
        --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-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/OpenMath-Nemotron-1.5B with Docker Model Runner:
```
docker model run hf.co/nvidia/OpenMath-Nemotron-1.5B
```

igitman commited on Apr 23, 2025

Commit

681f426

verified ·

1 Parent(s): 8ae0257

Update README.md

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README.md +6 -6

README.md CHANGED Viewed

@@ -17,7 +17,7 @@ library_name: transformers
 OpenMath-Nemotron-1.5B is created by finetuning [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) dataset.
 This model is ready for commercial use.
-![Accuracy Plot](./accuracy_plot.png)
 OpenMath-Nemotron models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1
@@ -27,10 +27,10 @@ Please see our [paper](TODO) for more details on the evaluation setup.
 | Model                         | AIME24 |  AIME25     |  HMMT-24-25     | HLE-Math    |
 |-------------------------------|-----------------|-------|-------|-------------|
 | DeepSeek-R1-Distill-Qwen-1.5B | 26.8 (60.0)     | 21.4 (36.7) | 14.2 (26.5) | 2.9 (5.0)   |
-| **[OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) CoT**   | 61.6 (80.0)     | 49.5 (66.7) | 39.9 (53.6) | 5.4 (5.4)   |
-| **[OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) TIR**   | 52.0 (83.3)     | 39.7 (70.0) | 37.2 (60.7) | 2.5 (6.2)   |
-| **+ Self GenSelect**              | 83.3            | 70.0  | 62.2  | 7.9         |
-| **+ 32B GenSelect**               | 83.3            | 70.0  | 62.8  | 8.3         |
 | DeepSeek-R1-Distill-Qwen-7B  | 54.4 (80.0)     | 38.6 (53.3) | 30.6 (42.9) | 3.3 (5.2)   |
 | [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) CoT    | 74.8 (80.0)     | 61.2 (76.7) | 49.7 (57.7) | 6.6 (6.6)   |
 | [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) TIR    | 72.9 (83.3)     | 57.5 (76.7) | 54.6 (66.3) | 7.8 (10.8)  |
@@ -211,6 +211,6 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
-For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./EXPLAINABILITY.md), [Bias](./BIAS.md), [Safety & Security](./SAFETY_and_SECURITY.md), and [Privacy](./PRIVACY.md) Subcards.
 Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

 OpenMath-Nemotron-1.5B is created by finetuning [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) dataset.
 This model is ready for commercial use.
+![Evaluation Results](./results.png)
 OpenMath-Nemotron models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1
 | Model                         | AIME24 |  AIME25     |  HMMT-24-25     | HLE-Math    |
 |-------------------------------|-----------------|-------|-------|-------------|
 | DeepSeek-R1-Distill-Qwen-1.5B | 26.8 (60.0)     | 21.4 (36.7) | 14.2 (26.5) | 2.9 (5.0)   |
+| [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) CoT   | 61.6 (80.0)     | 49.5 (66.7) | 39.9 (53.6) | 5.4 (5.4)   |
+| [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) TIR   | 52.0 (83.3)     | 39.7 (70.0) | 37.2 (60.7) | 2.5 (6.2)   |
+| + Self GenSelect              | 83.3            | 70.0  | 62.2  | 7.9         |
+| + 32B GenSelect               | 83.3            | 70.0  | 62.8  | 8.3         |
 | DeepSeek-R1-Distill-Qwen-7B  | 54.4 (80.0)     | 38.6 (53.3) | 30.6 (42.9) | 3.3 (5.2)   |
 | [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) CoT    | 74.8 (80.0)     | 61.2 (76.7) | 49.7 (57.7) | 6.6 (6.6)   |
 | [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) TIR    | 72.9 (83.3)     | 57.5 (76.7) | 54.6 (66.3) | 7.8 (10.8)  |
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./EXPLAINABILITY.md), [Bias](./BIAS.md), [Safety & Security](./SAFETY.md), and [Privacy](./PRIVACY.md) Subcards.
 Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).