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README.md

@@ -1,47 +1,49 @@
 ---
-license: Apache License 2.0
-
-#model-type:
-##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
-
-#domain:
-##如 nlp、cv、audio、multi-modal
-#- nlp
-
-#language:
-##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
-
-#metrics:
-##如 CIDEr、Blue、ROUGE 等
-#- CIDEr
-
-#tags:
-##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
-
-#tools:
-##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
+library_name: transformers
+datasets:
+- agentica-org/DeepScaleR-Preview-Dataset
+- agentica-org/DeepCoder-Preview-Dataset
+base_model:
+- nvidia/AceReason-Nemotron-14B
+license: cc-by-nc-4.0
 ---
-### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
 
-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
-```
-```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('Salesforce/E1-AceReason-14B')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/Salesforce/E1-AceReason-14B.git
+## Introduction
+E1-AceReason-14B is a language model fine-tuned from AceReason-Nemotron-14B. It is trained for Elastic Reasoning by budget-constrained rollout strategy, integrated into GRPO, which teaches the model to reason adaptively when the thinking process is cut short and generalizes effectively to unseen budget constraints without additional training.
+
+## Usage
+For detailed usage, please refer to [repo](https://github.com/SalesforceAIResearch/Elastic-Reasoning).
+
+## Performance on AIME24 (Avg@16)
+
+Note: We did not tune the training hyperparameters. The performance may slightly differ from the results reported in the original paper due to differences in the environment setup.
+
+| Model | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) |
+|---------------|--------------|---------------|--------------|---------------|--------------|---------------|--------------|---------------|--------------|---------------|
+| AceReason-Nemotron-14B | 13833 | 76.5 | - | -  | - | - | - | - | - | - |
+| E1-AceReason-14B  | 8376 | 75.4 | 1318 | 13.3 | 1736 | 22.7 | 2660  | 33.8 | 3448 | 44.6 |
+
+## Performance on LiveCodeBenchv5 (Avg@8)
+
+Note: We did not tune the training hyperparameters. The performance may slightly differ from the results reported in the original paper due to differences in the environment setup.
+
+| Model | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) |
+|---------------|--------------|---------------|--------------|---------------|--------------|---------------|--------------|---------------|--------------|---------------|
+| AceReason-Nemotron-14B | 16669 | 59.2 | - | -  | - | - | - | - | - | - |
+| E1-AceReason-14B  | 10796 | 57.8 | 1314 | 31.3 | 1810 | 36.3 | 2743  | 40.2 | 3585 | 43.5 |
+
+
+## Citation
+
+
+```bibtex
+@article{xu2025scalable,
+  title={Scalable Chain of Thoughts via Elastic Reasoning},
+  author={Xu, Yuhui and Dong, Hanze and Wang, Lei and Sahoo, Doyen and Li, Junnan and Xiong, Caiming},
+  journal={arXiv preprint arXiv:2505.05315},
+  year={2025}
+}
 ```
 
-<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+## Ethical Considerations
+This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people's lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.