Fast-Math-Qwen3-14B

By applying SFT and GRPO on difficult math problems, we enhanced the performance of DeepSeek-R1-Distill-Qwen-14B and developed Fast-Math-R1-14B, which achieves approx. 30% faster inference on average, while maintaining accuracy.

In addition, we trained and open-sourced Fast-Math-Qwen3-14B, an efficiency-optimized version of Qwen3-14B`, following the same approach.

Compared to Qwen3-14B, this model enables approx. 65% faster inference on average, with minimal loss in performance.

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)