Instructions to use RabotniKuma/Fast-Math-Qwen3-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RabotniKuma/Fast-Math-Qwen3-14B with Transformers:

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

pipe = pipeline("text-generation", model="RabotniKuma/Fast-Math-Qwen3-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RabotniKuma/Fast-Math-Qwen3-14B")
model = AutoModelForCausalLM.from_pretrained("RabotniKuma/Fast-Math-Qwen3-14B")
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-Qwen3-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RabotniKuma/Fast-Math-Qwen3-14B"
# 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-Qwen3-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RabotniKuma/Fast-Math-Qwen3-14B

SGLang

How to use RabotniKuma/Fast-Math-Qwen3-14B 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-Qwen3-14B" \
    --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-Qwen3-14B",
		"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-Qwen3-14B" \
        --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-Qwen3-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RabotniKuma/Fast-Math-Qwen3-14B with Docker Model Runner:
```
docker model run hf.co/RabotniKuma/Fast-Math-Qwen3-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Fast-Math-Qwen3-14B

Fast-Math-Qwen3-14B is an efficiency-optimized version of Qwen3-14B, developed following the two-stage recipe of Supervised Fine-Tuning (SFT) and Reinforcement Learning from Online Inference (GRPO) presented in the paper:

A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning

This model enables approx. 65% faster inference on average, with minimal loss in performance, compared to the base Qwen3-14B.

Technical details can be found in our github repository.

Note: This model likely inherits the ability to perform inference in TIR mode from the original model. However, all of our experiments were conducted in CoT mode, and its performance in TIR mode has not been evaluated.

Evaluation

		AIME 2024		AIME 2025
Model	Token budget	Pass@1 (avg. 64)	Mean output tokens	Pass@1 (avg. 64)	Mean output tokens
Qwen3-14B	32000	79.3	13669	69.5	16481
	24000	75.9	13168	65.6	15235
	16000	64.5	11351	50.4	12522
	12000	49.7	9746	36.3	10353
	8000	28.4	7374	19.5	7485
Fast-Math-Qwen3-14B	32000	77.6	9740	66.6	12281
	24000	76.5	9634	65.3	11847
	16000	72.6	8793	60.1	10195
	12000	65.1	7775	49.4	8733
	8000	50.7	6260	36	6618

Inference

vLLM

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_path = 'RabotniKuma/Fast-Math-Qwen3-14B'
vllm_engine = LLM(
    model=model_path,
    max_model_len=16000,
    gpu_memory_utilization=0.9,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
sampling_params = SamplingParams(
    temperature=1.0,
    top_p=0.90,
    min_p=0.05,
    max_tokens=8192,
    stop='</think>',  # For even faster inference, applying early stopping at the </think> tag and extracting the final boxed content is recommended.
)
messages = [
    {
        'role': 'user',
        'content': (
            'Solve the problem, and put the answer in \\boxed{{}}. '
            'Sarah is twice as old as her youngest brother. If the difference between their ages is 15 years. How old is her youngest brother?'
        )
    }
]
messages = tokenizer.apply_chat_template(
    conversation=messages,
    tokenize=False,
    add_generation_prompt=True
)
response = vllm_engine.generate(messages, sampling_params=sampling_params)