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

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
```

Add pipeline tag: text-generation

by nielsr HF Staff - opened Apr 25, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-4

Files changed (1) hide show

README.md +7 -4

README.md CHANGED Viewed

@@ -1,15 +1,16 @@
 ---
-license: cc-by-4.0
 base_model:
 - Qwen/Qwen2.5-Math-1.5B
 datasets:
 - nvidia/OpenMathReasoning
 language:
 - en
 tags:
 - nvidia
 - math
-library_name: transformers
 ---
 # OpenMath-Nemotron-1.5B
@@ -86,7 +87,9 @@ pipeline = transformers.pipeline(
 messages = [
     {
         "role": "user",
-        "content": "Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}.\n\n" +
         "What is the minimum value of $a^2+6a-7$?"},
 ]
@@ -215,4 +218,4 @@ NVIDIA believes Trustworthy AI is a shared responsibility and we have establishe
 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/).

 ---
 base_model:
 - Qwen/Qwen2.5-Math-1.5B
 datasets:
 - nvidia/OpenMathReasoning
 language:
 - en
+library_name: transformers
+license: cc-by-4.0
 tags:
 - nvidia
 - math
+pipeline_tag: text-generation
 ---
 # OpenMath-Nemotron-1.5B
 messages = [
     {
         "role": "user",
+        "content": "Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}.
+" +
         "What is the minimum value of $a^2+6a-7$?"},
 ]
 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/).