Instructions to use nvidia/OpenMath-Nemotron-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/OpenMath-Nemotron-14B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/OpenMath-Nemotron-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/OpenMath-Nemotron-14B")
model = AutoModelForCausalLM.from_pretrained("nvidia/OpenMath-Nemotron-14B")
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-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/OpenMath-Nemotron-14B"
# 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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/OpenMath-Nemotron-14B

SGLang

How to use nvidia/OpenMath-Nemotron-14B 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-14B" \
    --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-14B",
		"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-14B" \
        --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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/OpenMath-Nemotron-14B with Docker Model Runner:
```
docker model run hf.co/nvidia/OpenMath-Nemotron-14B
```

igitman commited on Apr 24, 2025

Commit

8e85e6c

verified ·

1 Parent(s): 593fbbe

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -22,7 +22,7 @@ This model is ready for commercial use.
 OpenMath-Nemotron models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1
 is an average accuracy across 64 generations and maj@64 is the result of majority voting.
-Please see our [paper](https://github.com/NVIDIA/NeMo-Skills/blob/main/recipes/openmathreasoning.pdf) for more details on the evaluation setup.
 | Model                         | AIME24 |  AIME25     |  HMMT-24-25     | HLE-Math    |
 |-------------------------------|-----------------|-------|-------|-------------|
@@ -59,6 +59,7 @@ The pipeline we used to produce the data and models is fully open-sourced!
 - [Code](https://github.com/NVIDIA/NeMo-Skills)
 - [Models](https://huggingface.co/collections/nvidia/openmathreasoning-68072c0154a5099573d2e730)
 - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathReasoning)
 We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/)
 to fully reproduce our results, including data generation.
@@ -111,7 +112,7 @@ If you find our work useful, please consider citing us!
   title   = {AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset},
   author  = {Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman},
   year    = {2025},
-  journal = {arXiv preprint arXiv:TBD}
 }
 ```

 OpenMath-Nemotron models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1
 is an average accuracy across 64 generations and maj@64 is the result of majority voting.
+Please see our [paper](https://arxiv.org/abs/2504.16891) for more details on the evaluation setup.
 | Model                         | AIME24 |  AIME25     |  HMMT-24-25     | HLE-Math    |
 |-------------------------------|-----------------|-------|-------|-------------|
 - [Code](https://github.com/NVIDIA/NeMo-Skills)
 - [Models](https://huggingface.co/collections/nvidia/openmathreasoning-68072c0154a5099573d2e730)
 - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathReasoning)
+- [Paper](https://arxiv.org/abs/2504.16891)
 We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/)
 to fully reproduce our results, including data generation.
   title   = {AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset},
   author  = {Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman},
   year    = {2025},
+  journal = {arXiv preprint arXiv:2504.16891}
 }
 ```