Improve model card for Agentic-R1

This PR significantly improves the model card for `VanishD/Agentic-R1` by:

- Adding the `pipeline_tag: text-generation` to ensure the model appears in relevant searches on the Hub.
- Adding `library_name: transformers` to enable the automated "How to use" code snippet, as the model is compatible with the `transformers` library (evidenced by `config.json` and typical Hugging Face model structure).
- Adding descriptive `tags` such as `qwen2`, `reasoning`, `tool-use`, and `llm` for better discoverability.
- Including a direct link to the paper: [Agentic-R1: Distilled Dual-Strategy Reasoning](https://huggingface.co/papers/2507.05707).
- Adding a link to the GitHub repository: `https://github.com/StigLidu/DualDistill`.
- Replacing the placeholder content with a comprehensive description derived from the paper abstract and the GitHub README, including:
- The paper abstract.
- Key Features.
- Datasets.
- Performance Results.
- Detailed Quick Start instructions (Installation, Inference Server, and Evaluation scripts) directly from the GitHub README.
- Information on Trained Models.
- Important Notes, License, Acknowledgments, and Contact details.
- The academic citation.

*Note*: A Python inference code snippet was not added to the "Quick Start" section as it was not explicitly found in the provided GitHub README, in strict adherence to the instructions.

README.md

@@ -1,7 +1,143 @@
 ---
-license: mit
-language:
-- en
 base_model:
 - VanishD/Agentic-R1
----
+language:
+- en
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- qwen2
+- reasoning
+- tool-use
+- llm
+---
+
+# Agentic-R1: Distilled Dual-Strategy Reasoning
+
+The model was presented in the paper [Agentic-R1: Distilled Dual-Strategy Reasoning](https://huggingface.co/papers/2507.05707).
+
+Code: https://github.com/StigLidu/DualDistill
+
+## Abstract
+
+Current long chain-of-thought (long-CoT) models excel at mathematical reasoning but rely on slow and error-prone natural language traces. Tool-augmented agents address arithmetic via code execution, but often falter on complex logical tasks. We introduce a fine-tuning framework, DualDistill, that distills complementary reasoning strategies from multiple teachers into a unified student model. Using this approach, we train Agentic-R1, which dynamically selects the optimal strategy for each query, invoking tools for arithmetic and algorithmic problems, and using text-based reasoning for abstract ones. Our method improves accuracy across a range of tasks, including both computation-intensive and standard benchmarks, demonstrating the effectiveness of multi-strategy distillation in achieving robust and efficient reasoning.
+
+## Key Features
+
+-   **Efficient Training**: Integrates tool use into long-chain-of-thought (CoT) reasoning using only 4 × A6000 GPUs
+-   **Unified Reasoning**: Fuses heterogeneous reasoning traces from multiple teacher models into a single student model
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/StigLidu/DualDistill/main/fig/overview.png" alt="Overview of DualDistill methodology" width="500">
+  <p><em>Overview of DualDistill methodology</em></p>
+</div>
+
+## Datasets
+
+| Dataset | Description | Link |
+|---------|-------------|------|
+| **Training Set** | Complete training dataset with teacher trajectories | [🤗 HuggingFace](https://huggingface.co/datasets/VanishD/DualDistill) |
+| **Test Set** | Evaluation benchmarks | `dataset/test/` |
+
+## Results
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/StigLidu/DualDistill/main/fig/result.png" alt="Performance comparison of Agentic-R1 models" width="700">
+</div>
+
+-   **Agentic-R1** demonstrates significant performance gains on **DeepMath-L** and **Combinatorics300**, where both complex reasoning and tool use are crucial for success.
+-   **Agentic-R1-SD** (Self-Distilled) further enhances performance through our self-distillation approach, consistently outperforming baseline models across nearly all evaluation tasks.
+
+## Quick Start
+
+### Installation
+
+1.  **Clone the repository**:
+    ```bash
+    git clone https://github.com/StigLidu/DualDistill.git
+    cd DualDistill
+    ```
+
+2.  **Create environment** (optional but recommended):
+    ```bash
+    conda create -n dualdistill python=3.11
+    conda activate dualdistill
+    ```
+
+3.  **Install dependencies**:
+    ```bash
+    pip install -r requirements.txt
+    pip install flash-attn --no-build-isolation
+    ```
+
+### Inference Server and Evaluation
+
+To run inference and evaluation using the provided scripts:
+
+1.  **Start inference server**:
+    ```bash
+    bash script/eval_script/start_inference_server.sh [model_path] [display_name] [port]
+    ```
+
+2.  **Run Evaluation**:
+    ```bash
+    bash script/eval_script/eval_remote_server.sh \
+      [url] [display_name] [data_path] [code_mode] [max_token]
+    ```
+
+    **Example**:
+    ```bash
+    bash script/eval_script/eval_remote_server.sh \
+      "http://localhost:8080/v1" "agentic-r1" "dataset/test/math.json" "true" "4096"
+    ```
+
+## Trained Models
+
+| Model | Description | HuggingFace Link |
+|-------|-------------|------------------|
+| **Agentic-R1-7B** | Base model with teacher distillation | [🤗 Download](https://huggingface.co/VanishD/Agentic-R1) |
+| **Agentic-R1-7B-SD** | Enhanced model with self-distillation | [🤗 Download](https://huggingface.co/VanishD/Agentic-R1-SD) |
+
+## ⚠️ Important Notes
+
+-   **Code Execution Safety**: The evaluation scripts execute model-generated code locally. Only use trusted models before execution.
+-   **Inference Config**: If you are using vLLM (a recent version) and encounter an error regarding the maximum context length. You may need to modify the `model_max_length` in `tokenizer_config.json`.
+-   **Self-Distillation Warning**: The self-distillation step requires sampling many trajectories and can be time-consuming.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgments
+
+We thank the following open-source projects for their foundational contributions:
+
+-   [OpenHands](https://github.com/All-Hands-AI/OpenHands) - Agent framework
+-   [DeepMath-103K](https://huggingface.co/datasets/zwhe99/DeepMath-103K) - Mathematical reasoning dataset
+-   [vLLM](https://github.com/vllm-project/vllm) - High-performance inference engine
+
+## Contact
+
+For questions or support, please contact:
+
+-   **Weihua Du**: [weihuad@cs.cmu.edu](mailto:weihuad@cs.cmu.edu)
+
+## Citation
+
+If you find our work useful, please consider citing:
+
+```bibtex
+@article{du2025agentic,
+  title={Agentic-R1: Distilled Dual-Strategy Reasoning},
+  author={Du, Weihua and Aggarwal, Pranjal and Welleck, Sean and Yang, Yiming},
+  journal={arXiv preprint arXiv:2507.05707},
+  year={2025}
+}
+```
+
+---
+
+<div align="center">
+  <p>⭐ Star us on GitHub if this project helped you!</p>
+</div>