Add model card and metadata

Hi! I'm Niels from the community science team at Hugging Face. I've noticed this repository doesn't have a complete model card. This PR adds metadata linking the repository to the [MePo paper](https://huggingface.co/papers/2602.07940), sets the appropriate pipeline tag for classification, and provides a summary of the method along with links to the official code and citation info.

README.md

@@ -1,3 +1,46 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-classification
+tags:
+- continual-learning
+- meta-learning
+- computer-vision
+---
+
+# MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning
+
+This repository contains the model checkpoints and artifacts for **MePo** (**Me**ta **Po**st-Refinement), as presented in the paper [MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning](https://huggingface.co/papers/2602.07940).
+
+## Introduction
+
+**MePo** is an innovative approach for Pretrained Models (PTMs)-based General Continual Learning (GCL). It addresses the challenges of online datastreams and blurry task boundaries by refining the pretrained backbone before downstream continual learning. Inspired by meta-plasticity and reconstructive memory in neuroscience, MePo adapts representation learning to facilitate rapid adaptation to evolving environments in a rehearsal-free manner.
+
+## Resources
+
+- **Paper:** [MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning](https://huggingface.co/papers/2602.07940)
+- **GitHub Repository:** [SunGL001/MePo](https://github.com/SunGL001/MePo)
+
+## Methodology Overview
+
+MePo improves GCL performance through three core components:
+1. **Meta-Learning for Representation Learning**: A bi-level meta-learning paradigm that constructs pseudo task sequences from pretraining data to refine the backbone.
+2. **Meta Covariance Initialization**: Initializes a meta covariance matrix as the reference geometry of the pretrained representation space, enabling the exploitation of second-order statistics.
+3. **Feature Alignment**: Ensures robust output alignment during downstream tasks.
+
+The method serves as a plug-in strategy that achieves significant gains across various benchmarks such as CIFAR-100, ImageNet-R, and CUB-200.
+
+## Citation
+
+If you find this work useful for your research, please cite:
+
+```bibtex
+@misc{sun2026mepometapostrefinementrehearsalfree,
+      title={MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learnin}, 
+      author={Guanglong Sun and Hongwei Yan and Liyuan Wang and Zhiqi Kang and Shuang Cui and Hang Su and Jun Zhu and Yi Zhong},
+      year={2026},
+      eprint={2602.07940},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2602.07940}, 
+}
+```