Update README.md

README.md

@@ -1,199 +1,179 @@
 ---
-library_name: transformers
-tags: []
+license: mit
+datasets:
+- zwhe99/DeepMath-103K
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+tags:
+- math
+- reasoning
+- rl
+- qwen
+- qwen2
+model-index:
+- name: DeepMath-1.5B
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      name: MATH500
+      type: MATH500
+    metrics:
+    - type: pass@1
+      value: 0.899
+      name: pass@1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      name: AMC23
+      type: AMC23
+    metrics:
+    - type: pass@1
+      value: 0.823
+      name: pass@1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      name: OlympiadBench
+      type: OlympiadBench
+    metrics:
+    - type: pass@1
+      value: 0.618
+      name: pass@1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      name: MinervaMath
+      type: MinervaMath
+    metrics:
+    - type: pass@1
+      value: 0.425
+      name: pass@1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      name: AIME24
+      type: AIME24
+    metrics:
+    - type: pass@1
+      value: 0.373
+      name: pass@1
+      verified: false
+    - type: pass@1
+      value: 0.308
+      name: pass@1
+      verified: false
 ---
 
-# Model Card for Model ID
+# DeepMath-Zero-7B
+
+<table>
+  <tr>
+    <td style="padding: 0;">
+      <a href="https://huggingface.co/datasets/zwhe99/DeepMath-103K">
+        <img src="https://img.shields.io/badge/Data-4d5eff?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor" alt="Data">
+      </a>
+    </td>
+    <td style="padding: 0;">
+      <a href="https://huggingface.co/collections/zwhe99/deepmath-6816e139b7f467f21a459a9a">
+        <img src="https://img.shields.io/badge/Model-4d5eff?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor" alt="Data">
+      </a>
+    </td>
+    <td style="padding: 0;">
+      <a href="https://github.com/zwhe99/DeepMath">
+        <img src="https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white" alt="Code">
+      </a>
+    </td>
+    <td style="padding: 0;">
+      <a href="https://arxiv.org/abs/2504.11456">
+        <img src="https://img.shields.io/badge/arXiv-2504.11456-b31b1b.svg?style=for-the-badge" alt="arXiv">
+      </a>
+    </td>
+  </tr>
+</table>
 
 <!-- Provide a quick summary of what the model is/does. -->
+DeepMath-Zero-7B is created by finetuning Qwen/Qwen2.5-7B on DeepMath-103K dataset via RL.
 
+## 📖 Overview
 
+**`DeepMath-103K`** is meticulously curated to push the boundaries of mathematical reasoning in language models. Key features include:
 
-## Model Details
+**1. Challenging Problems**: DeepMath-103K has a strong focus on difficult mathematical problems (primarily Levels 5-9), significantly raising the complexity bar compared to many existing open datasets.
 
-### Model Description
+<div align="center"> <img src="./assets/github-difficulty.png" width="90%"/>
 
-<!-- Provide a longer summary of what this model is. -->
+<sub>Difficulty distribution comparison.</sub> </div>
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+**2. Broad Topical Diversity**: The dataset spans a wide spectrum of mathematical subjects, including Algebra, Calculus, Number Theory, Geometry, Probability, and Discrete Mathematics.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+<div align="center"> <img src="./assets/github-domain.png" width="50%"/>
 
-### Model Sources [optional]
+<sub>Hierarchical breakdown of mathematical topics covered in DeepMath-103K.</sub></div>
 
-<!-- Provide the basic links for the model. -->
+**4. Rigorous Decontamination**: Built from diverse sources, the dataset underwent meticulous decontamination against common benchmarks using semantic matching. This minimizes test set leakage and promotes fair model evaluation.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+<div align="center"> <img src="./assets/github-contamination-case.png" width="80%"/>
 
-## Uses
+<sub>Detected contamination examples. Subtle conceptual overlaps can also be identified.</sub> </div>
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+**5. Rich Data Format**: Each sample in `DeepMath-103K` is structured with rich information to support various research applications:
 
-### Direct Use
+<div align="center"> <img src="./assets/github-data-sample.png" width="90%"/>
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+<sub>A data sample from DeepMath-103K.</sub> </div>
 
-[More Information Needed]
+- **Question**: The mathematical problem statement.
+- **Final Answer**: A reliably verifiable final answer, enabling robust rule-based reward functions for RL.
+- **Difficulty**: A numerical score for difficulty-aware training or analysis.
+- **Topic**: Hierarchical classification for topic-specific applications.
+- **R1 Solutions**: Three distinct reasoning paths from DeepSeek-R1, valuable for supervised fine-tuning (SFT) or knowledge distillation.
 
-### Downstream Use [optional]
+## 📊Main Results
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+`DeepMath-Zero-7B` and `DeepMath-1.5B` are trained on the `DeepMath-103K` dataset via RL. These models are initialized from `Qwen2.5-7B-Base` and `R1-Distill-Qwen-1.5B`, respectively.
 
-[More Information Needed]
 
-### Out-of-Scope Use
+|          Model           | MATH 500 |  AMC23   | Olympiad Bench | Minerva Math |  AIME24  |  AIME25  |
+| :----------------------: | :------: | :------: | :------------: | :----------: | :------: | :------: |
+|     Qwen2.5-7B-Base      |   54.8   |   35.3   |      27.8      |     16.2     |   7.7    |   5.4    |
+|  Open-Reasoner-Zero-7B   |   81.8   |   58.9   |      47.9      |     38.4     |   15.6   |   14.4   |
+| Qwen-2.5-7B-SimpleRL-Zoo |   77.0   |   55.8   |      41.0      |     41.2     |   15.6   |   8.7    |
+|     DeepMath-Zero-7B     | **85.5** | **64.7** |    **51.0**    |   **45.3**   | **20.4** | **17.5** |
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+|          Model          | MATH 500 |  AMC23   | Olympiad Bench | Minerva Math |  AIME24  |  AIME25  |
+| :---------------------: | :------: | :------: | :------------: | :----------: | :------: | :------: |
+|  R1-Distill-Qwen-1.5B   |   84.7   |   72.0   |      53.1      |     36.6     |   29.4   |   24.8   |
+| DeepScaleR-1.5B-Preview |   89.4   |   80.3   |      60.9      |     42.2     | **42.3** |   29.6   |
+|  Still-3-1.5B-Preview   |   86.6   |   75.8   |      55.7      |     38.7     |   30.8   |   24.6   |
+|   DeepMath-1.5B         | **89.9** | **82.3** |    **61.8**    |   **42.5**   |   37.3   | **30.8** |
 
-[More Information Needed]
+## 🙏 Acknowledgements
 
-## Bias, Risks, and Limitations
+This work can not be done without the help of the following works:
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- **[verl](https://github.com/volcengine/verl)**: A very fast reinforcement learning framework.
+- **[Vivacem/MMIQC](https://huggingface.co/datasets/Vivacem/MMIQC)**: A mixture of question-response pairs extracted from Mathematics Stack Exchange pages.
+- **[TIGER-Lab/WebInstructSub](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)**: Instruction data from MathStackExchange and ScienceStackExchange.
+- **[AI-MO/NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT)**: Approximately 860k math problems.
 
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+## 📚 Citation
+```bibtex
+@article{deepmath,
+  title={DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and  Verifiable Mathematical Dataset for Advancing Reasoning},
+  author={He, Zhiwei and Liang, Tian and Xu, Jiahao and Liu, Qiuzhi and Chen, Xingyu and Wang, Yue and Song, Linfeng and Yu, Dian and Liang, Zhenwen and Wang, Wenxuan and Zhang, Zhuosheng and Wang, Rui and Tu, Zhaopeng and Mi, Haitao and Yu, Dong},
+  year={2025},
+  eprint={2504.11456},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2504.11456}, 
+}
+```