Create README.md

README.md

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+---
+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.