README.md

---
license: mit
base_model:
- Qwen/Qwen2.5-7B-Instruct-1M
---

### Model Card: Graph-R1 Series

This model card covers the Graph-R1 series of models, including the final released versions and variants used in ablation studies. All information is based on the provided research paper.

#### **Model Details**

* **Model Developer**: HKUST-DSAIL
* **Model Series**: Graph-R1
* **Model Variants**:
    * **Graph-R1-7B**: Fine-tuned from Qwen2.5-7B-Instruct-1M.
    * **Graph-R1-1.5B**: Fine-tuned from Qwen2.5-1.5B.
    * **Ablation Models**: Multiple variants based on different training configurations (e.g., data volume, training stages, reward functions, curriculum learning strategies).
* **Model Type**: Small reasoning language model, specialized in solving complex NP graph-theoretic problems.
* **Architecture**:
    * **Base Model**: Qwen2.5
    * **Training Framework**:
        1.  **Cold-start Supervised Fine-Tuning (SFT)**: Fine-tuned using long Chain-of-Thought (Long-CoT) data extracted from the QwQ-32B model to inject graph reasoning knowledge.
        2.  **Reasoning Optimization via Reinforcement Learning (RL)**: Employs a Group Relative Policy Optimization (GRPO)-based RL framework, combined with a curriculum learning strategy. 
* **Model Date**: 2025/04

#### **Intended Use**

* **Primary Use Cases**:
    * Solving complex graph-theoretic computational problems at the NP-Complete level, such as the Traveling Salesman Problem (TSP), Graph Edit Distance (GED), and Maximum Clique Problem (MCP).
    * Serving as a compact, resource-efficient reasoning model for academic research and practical applications. 
* **Potential Cross-Domain Applications**:
    * The model demonstrates transferability to other complex reasoning tasks, including mathematics, programming, STEM, and logical reasoning.