ReasonFlux-F1-7B Open-Source Large Language Model - Powerful Reasoning Ability Helps Complete Multiple Reasoning Tasks

Reasonflux F1 7B

Developed by Gen-Verse

ReasonFlux-F1-7B is a hierarchical large language model reasoning model based on thinking template expansion, fine-tuned through template-enhanced reasoning trajectories, demonstrating outstanding performance in multiple reasoning tasks.

Large Language Model

Transformers

Open Source License:Other #Mathematical Reasoning #Competition Problem Solving #Thinking Template Enhancement

Downloads 291

Release Time : 3/22/2025

Model Overview

This model adopts a revolutionary template-enhanced reasoning paradigm, focusing on improving the performance of large language models in complex reasoning tasks, particularly excelling in mathematical and logical reasoning.

Model Features

Template-Enhanced Reasoning

Adopts a revolutionary template-enhanced reasoning paradigm, significantly improving the model's performance in complex reasoning tasks.

Hierarchical Reasoning Architecture

A hierarchical reasoning architecture based on thinking template expansion, capable of handling complex multi-step reasoning problems.

High-Performance Reasoning

Outperforms peer models in multiple reasoning benchmarks, including AIME, MATH500, and GPQA-Diamond.

Model Capabilities

Mathematical Problem Solving

Logical Reasoning

Complex Problem Analysis

Multi-Step Reasoning

Use Cases

Mathematics Competitions

AIME Competition Problem Solutions

Solving complex mathematical problems from the American Invitational Mathematics Examination (AIME).

Achieved a 76.7% pass rate in the AIME2024 test.

Academic Research

Advanced Mathematical Problem Solving

Solving graduate-level mathematical problems.

Achieved a 96.0% pass rate in the MATH500 test.

Logical Reasoning

Complex Logical Problem Analysis

Solving logical problems requiring multi-step reasoning.

Achieved a 67.2% pass rate in the GPQA-Diamond test.

🚀 ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates

Revolutionary template-augmented reasoning paradigm enables a 32B model to outperform o1-mini and DeepSeek-R1 distilled models in reasoning tasks.

Model Information

Property	Details
Library Name	transformers
License	other
Base Model	deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Tags	llama-factory, full, generated_from_trainer
Model Name	ReasonFlux-F1-7B

Performance Comparison

Task/Pass@1	ReasonFlux-F1-32B	ReasonFlux-Zero-32B	R1-Distill-32B	o1-mini	LIMO -32B	s1-32B
MATH500	96.0	91.2	94.3	90.0	90.6	93.0
AIME 2024	76.7	56.7	72.6	56.7	50.0	56.7
AIME 2025	53.3	37.2	46.67	50.8	37.2	49.3
GPQA-Diamond	67.2	61.2	62.1	60.0	65.2	59.6

✨ Features

ReasonFlux-F1-7B is our finetuned SOTA-level reasoning LLM by leveraging the template-augmented reasoning trajectories from our ReasonFlux-Zero.

Github Repository: Gen-Verse/ReasonFlux
Paper: ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
Dataset: Gen-Verse/ReasonFlux-F1-SFT

📊 Evaluation

We present the evaluation results of our ReasonFlux-F1-32B on challenging reasoning tasks including AIME2024, AIM2025, MATH500 and GPQA-Diamond. To make a fair comparison, we report the results of the LLMs on our evaluation scripts in ReasonFlux-F1.

Model	AIME2024@pass1	AIME2025@pass1	MATH500@pass1	GPQA@pass1
QwQ-32B-Preview	46.7	37.2	90.6	65.2
LIMO-32B	56.3	44.5	94.8	58.1
s1-32B	56.7	49.3	93.0	59.6
OpenThinker-32B	66.0	53.3	94.8	60.1
R1-Distill-32B	70.0	46.7	92.0	59.6
ReasonFlux-Zero-32B	56.7	37.2	91.2	61.2
ReasonFlux-F1-32B	76.7	53.3	96.0	67.2

🚀 Quick Start

💻 Usage Examples

Basic Usage

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = 'Gen-Verse/ReasonFlux-F1-7B'

model = LLM(
    model_id,
    tensor_parallel_size=8,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

sampling_params = SamplingParams(
    max_tokens=32768,
)
# 2022 AIME I Problems/Problem 15
question = """Let \(x, y\), and \(z\) be positive real numbers satisfying the system of equations:
\[
\begin{array}{c}
\sqrt{2 x-x y}+\sqrt{2 y-x y}=1 \\
\sqrt{2 y-y z}+\sqrt{2 z-y z}=\sqrt{2} \\
\sqrt{2 z-z x}+\sqrt{2 x-z x}=\sqrt{3} .
\end{array}
\]
Then \(\left[(1-x)(1-y)(1-z)\right]^{2}\) can be written as \(\frac{m}{n}\), where \(m\) and \(n\) are relatively prime positive integers. Find \(m+n\)."""
ds_prompt="<｜User｜>\n" + question + "<｜Assistant｜>\n"
output = model.generate(ds_prompt, sampling_params=sampling_params)
print(output[0].outputs[0].text)

📄 Citation

@article{yang2025reasonflux,
  title={ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates},
  author={Yang, Ling and Yu, Zhaochen and Cui, Bin and Wang, Mengdi},
  journal={arXiv preprint arXiv:2502.06772},
  year={2025}
}

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