Instructions to use RabotniKuma/Fast-Math-R1-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RabotniKuma/Fast-Math-R1-14B with Transformers:

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

pipe = pipeline("text-generation", model="RabotniKuma/Fast-Math-R1-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RabotniKuma/Fast-Math-R1-14B")
model = AutoModelForCausalLM.from_pretrained("RabotniKuma/Fast-Math-R1-14B")
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-Math-R1-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RabotniKuma/Fast-Math-R1-14B"
# 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-Math-R1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RabotniKuma/Fast-Math-R1-14B

SGLang

How to use RabotniKuma/Fast-Math-R1-14B 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-Math-R1-14B" \
    --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-Math-R1-14B",
		"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-Math-R1-14B" \
        --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-Math-R1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RabotniKuma/Fast-Math-R1-14B with Docker Model Runner:
```
docker model run hf.co/RabotniKuma/Fast-Math-R1-14B
```

Improve model card with abstract, GitHub link, and comprehensive download section

by nielsr HF Staff - opened Jul 19, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+29

-20

nielsr

Jul 19, 2025

This pull request aims to enhance the model card for Fast-Math-R1-14B by:

Adding the paper abstract for a more immediate and detailed understanding of the model.
Introducing a prominent "Code:" link to the GitHub repository for easier access to the project's codebase.
Replacing the current "Dataset" section with a more comprehensive "Download" section, sourced from the original GitHub repository, which includes links to the model itself and related models, in addition to the datasets.

These updates improve the discoverability of associated resources and the overall completeness of the model card.

Improve model card with abstract, GitHub link, and comprehensive download sectionf93e1466

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