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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RabotniKuma/Fast-OpenMath-Nemotron-14B")
model = AutoModelForCausalLM.from_pretrained("RabotniKuma/Fast-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RabotniKuma/Fast-OpenMath-Nemotron-14B with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Enhance model card with metadata, paper link, and project page

by nielsr HF Staff - opened Jul 14, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+26

-17

nielsr

Jul 14, 2025

This pull request improves the model card for Fast-OpenMath-Nemotron-14B by adding crucial metadata and relevant links:

Metadata:
- Adds pipeline_tag: text-generation for better discoverability on the Hub.
- Adds library_name: transformers to indicate compatibility with the Hugging Face Transformers library.
- Adds paper: https://huggingface.co/papers/2507.08267 to directly link to the associated research paper.
- Adds tags: [mathematical-reasoning, qwen2] to accurately categorize the model's domain and base architecture.
Content:
- Introduces the model with a clear link to its originating paper.
- Includes the project page URL for more comprehensive information.

These updates will make the model card more informative, discoverable, and aligned with Hugging Face Hub best practices.

Enhance model card with metadata, paper link, and project page50f1263b

RabotniKuma changed pull request status to merged Jul 15, 2025

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