README.md

---
library_name: transformers
license: other
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: ReasonFlux-F1-14B
  results: []
---

# ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
Revolutionary template-augmented reasoning paradigm enpowers a 32B model to outperform o1-mini and DeepSeek-R1 distilled models in reasoning tasks.

| 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    |

# ReasonFlux-F1-14B

> ReasonFlux-F1-14B is our finetuned SOTA-level reasoning LLM by leveraging the template-augmented reasoning trajectories from our [ReasonFlux-Zero](https://arxiv.org/abs/2502.06772). 

* Github Repository: [Gen-Verse/ReasonFlux](https://github.com/Gen-Verse/ReasonFlux)
* Paper:[ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates](https://arxiv.org/abs/2502.06772)
* Dataset: [Gen-Verse/ReasonFlux-F1-SFT](https://huggingface.co/datasets/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](https://github.com/Gen-Verse/ReasonFlux).

| 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 with VLLM
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = 'Gen-Verse/ReasonFlux-F1-14B'

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

```bash
@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}
}
```