Reasonflux F1 7B

🚀 ReasonFlux：通过扩展思维模板实现分层大语言模型推理

ReasonFlux是一种革命性的模板增强推理范式，它使一个32B的模型在推理任务中超越了o1 - mini和DeepSeek - R1蒸馏模型。

🚀 快速开始

使用VLLM快速开始

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)

✨ 主要特性

ReasonFlux - F1 - 7B是我们通过利用来自ReasonFlux - Zero的模板增强推理轨迹进行微调的SOTA级推理大语言模型。

• Github仓库：[Gen - Verse/ReasonFlux](https://github.com/Gen - Verse/ReasonFlux)
• 论文：ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
• 数据集：[Gen - Verse/ReasonFlux - F1 - SFT](https://huggingface.co/datasets/Gen - Verse/ReasonFlux - F1 - SFT)

📚 详细文档

评估结果

我们展示了ReasonFlux - F1 - 32B在包括AIME2024、AIM2025、MATH500和GPQA - Diamond等具有挑战性的推理任务上的评估结果。为了进行公平比较，我们报告了这些大语言模型在[ReasonFlux - F1](https://github.com/Gen - Verse/ReasonFlux)评估脚本上的结果。

📄 许可证

许可证类型：other

📦 模型信息

📖 引用信息

@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}
}