Create README.md

README.md

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+---
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+This is the model is trained using paper, [M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models](https://arxiv.org/abs/2504.10449).
+
+
+| **Model**                          | **AIME 2025** | **AIME 2024** | **MATH 500** | **AMC 2023** | **OlympiadBench** |
+|-----------------------------------|---------------|---------------|--------------|--------------|-------------------|
+| Qwen2.5-Math-7B-Instruct  (Transformer)        | –             | 13.3          | 79.8         | 50.6         | 40.7              |
+| rStar-Math-7B  (Transformer)                   | –             | 26.7          | 78.4         | 47.5         | 47.1              |
+| Eurus-2-7B-PRIME (Transformer)                 | –             | 26.7          | 79.2         | 57.8         | 42.1              |
+| Qwen2.5-7B-SimpleRL (Transformer)              | –             | 26.7          | 82.4         | 62.5         | 43.3              |
+| DeepSeek-R1-Distill-Qwen-1.5B (Transformer)    | 23.0          | 28.8          | 82.8         | 62.9         | 43.3              |
+| **M1-3B (Mamba Hybrid Models)**                | 23.5          | 28.5          | 84.0         | 62.8         | 47.3              |
+
+
+
+Code: https://github.com/jxiw/M1
+
+```
+@article{wang2025m1scalabletesttimecompute,
+  title={M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models}, 
+  author={Junxiong Wang and Wen-Ding Li and Daniele Paliotta and Daniel Ritter and Alexander M. Rush and Tri Dao},
+  journal={arXiv preprint arXiv:2504.10449},
+  year={2025},
+  url={https://arxiv.org/abs/2504.10449}, 
+}