Create README.md

README.md

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+---
+license: apache-2.0
+---
+# Model Card: ReLLM-C2
+
+## 1. Model Summary
+The **ReLLM-C2** model is a Large Language Model (LLM) specifically fine-tuned to act as a surrogate model for **multi-objective optimization** in computationally expensive optimization tasks. 
+
+It serves as a core modeling component within the **R2SAEA** (Reinforced Relation Surrogate-Assisted Evolutionary Algorithm) framework. Unlike general-purpose LLMs, ReLLM-C2 is designed to seamlessly integrate with Evolutionary Algorithms (EAs). By leveraging structured prompt templates containing decision variables and objective data, the model can perform zero-shot relationship reasoning to evaluate and classify candidate solutions in multi-objective optimization scenarios.
+
+## 2. Intended Use
+*   **Primary Application:** Relational-based surrogate modeling in multi-objective Evolutionary Algorithms.
+*   **Out-of-Scope Use:** ReLLM-C2 is heavily fine-tuned specifically for numerical optimization and relationship reasoning. It is **not** intended for general conversational chat, creative writing, or standard code generation tasks.
+
+## 3. Background & Related Work
+This model bridges the gap between **Large Language Models (LLMs)** and **Evolutionary Algorithms (EAs)**, addressing a critical bottleneck in the field of Surrogate-Assisted Evolutionary Algorithms (SAEAs):
+*   **The Problem with Traditional SAEAs:** Conventional machine learning surrogate models (such as Gaussian Processes or Random Forests) require being retrained from scratch at every single generation using new evaluated data, which introduces massive computational overhead.
+*   **Our Methodology:** Through the R2SAEA framework, we transform the relationship reasoning problem in optimization tasks into a **Reinforcement Learning (RL)** problem. 
+*   **Training Alignment:** ReLLM-C2 is trained using the **Group Relative Policy Optimization (GRPO)** algorithm. This aligns the LLM's reasoning capabilities directly with multi-objective optimization goals, granting it the ability to perform zero-shot classification across a wide range of unseen tasks. This eliminates the need for generation-by-generation retraining while significantly reducing the computational burden associated with using general-purpose LLMs.
+
+## 4. GitHub Repository
+To utilize ReLLM-C2 effectively, it should be deployed alongside the **R2SAEA framework**, which handles prompt structuring and the evolutionary loop. The framework provides implementations in both **Python** (via pymoo) and **MATLAB** (via PlatEMO).
+
+For deployment instructions, API configuration, and framework integration, please visit our official repository:
+*   **GitHub Repository:** [[R2SAEA](https://github.com/Septend9/R2SAEA)]
+
+## 5. License
+The ReLLM-C2 model and the associated R2SAEA framework are open-sourced under the **Apache License 2.0**.
+
+## 6. Citation
+If you use this model or the R2SAEA framework in your research, please cite our work:
+
+```bibtex
+@misc{r2saeagithub,
+  title={R2SAEA: Relation Reasoning with LLMs in Expensive Optimization},
+  author={Ye Lu, BingDong Li, Aimin Zhou, Hao Hao},
+  year={2026},
+}
+```