87 lines
4.1 KiB
Markdown
87 lines
4.1 KiB
Markdown
---
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library_name: transformers
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license: other
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: ReasonFlux-F1-7B
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results: []
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---
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# ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
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Revolutionary template-augmented reasoning paradigm enpowers a 32B model to outperform o1-mini and DeepSeek-R1 distilled models in reasoning tasks.
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| Task/Pass@1 | **ReasonFlux-F1-32B** | **ReasonFlux-Zero-32B** | **R1-Distill-32B** | **o1-mini** | **LIMO -32B** | **s1-32B** |
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| :------------- | :----------------: | :-------------: | :-------------------: | :-----------------: | :--------: | :--------: |
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| MATH500 | **96.0** | 91.2 | 94.3 | 90.0 | 90.6 | 93.0 |
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| AIME 2024 | **76.7** | 56.7 | 72.6 | 56.7 | 50.0 | 56.7 |
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| AIME 2025 | **53.3** | 37.2 | 46.67 | 50.8 | 37.2 | 49.3 |
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| GPQA-Diamond | **67.2** | 61.2 | 62.1 | 60.0 | 65.2 | 59.6 |
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# ReasonFlux-F1-7B
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> ReasonFlux-F1-7B is our finetuned SOTA-level reasoning LLM by leveraging the template-augmented reasoning trajectories from our [ReasonFlux-Zero](https://arxiv.org/abs/2502.06772).
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* Github Repository: [Gen-Verse/ReasonFlux](https://github.com/Gen-Verse/ReasonFlux)
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* Paper:[ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates](https://arxiv.org/abs/2502.06772)
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* Dataset: [Gen-Verse/ReasonFlux-F1-SFT](https://huggingface.co/datasets/Gen-Verse/ReasonFlux-F1-SFT)
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## Evaluation
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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).
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| Model | AIME2024@pass1 | AIME2025@pass1 | MATH500@pass1 | GPQA@pass1 |
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| --------------------------------------- | :--------------: | :--------------: | :-------------: | :----------: |
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| QwQ-32B-Preview | 46.7 | 37.2 | 90.6 | 65.2 |
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| LIMO-32B | 56.3 | 44.5 | 94.8 | 58.1 |
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| s1-32B | 56.7 | 49.3 | 93.0 | 59.6 |
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| OpenThinker-32B | 66.0 | 53.3 | 94.8 | 60.1 |
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| R1-Distill-32B | 70.0 | 46.7 | 92.0 | 59.6 |
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| ReasonFlux-Zero-32B | 56.7 | 37.2 | 91.2 | 61.2 |
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| **ReasonFlux-F1-32B** | **76.7** | **53.3** | **96.0** | **67.2** |
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## Quick start with VLLM
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_id = 'Gen-Verse/ReasonFlux-F1-7B'
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model = LLM(
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model_id,
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tensor_parallel_size=8,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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sampling_params = SamplingParams(
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max_tokens=32768,
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)
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# 2022 AIME I Problems/Problem 15
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question = """Let \(x, y\), and \(z\) be positive real numbers satisfying the system of equations:
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\[
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\begin{array}{c}
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\sqrt{2 x-x y}+\sqrt{2 y-x y}=1 \\
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\sqrt{2 y-y z}+\sqrt{2 z-y z}=\sqrt{2} \\
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\sqrt{2 z-z x}+\sqrt{2 x-z x}=\sqrt{3} .
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\end{array}
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\]
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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 \(m+n\)."""
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ds_prompt="<｜User｜>\n" + question + "<｜Assistant｜>\n"
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output = model.generate(ds_prompt, sampling_params=sampling_params)
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print(output[0].outputs[0].text)
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```
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## Citation
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```bash
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@article{yang2025reasonflux,
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title={ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates},
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author={Yang, Ling and Yu, Zhaochen and Cui, Bin and Wang, Mengdi},
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journal={arXiv preprint arXiv:2502.06772},
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year={2025}
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}
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``` |