Add model card and metadata

README.md

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+---
+pipeline_tag: image-classification
+license: mit
+---
+
+# Breaking the Low-Rank Dilemma of Linear Attention: RAVLT Model Card
+
+This model card describes the Rank-Augmented Vision Linear Transformer (RAVLT), introduced in the paper "[Breaking the Low-Rank Dilemma of Linear Attention](https://arxiv.org/abs/2411.07635)". RAVLT achieves state-of-the-art performance on ImageNet-1k classification while maintaining linear complexity.
+
+**Key Features:**
+
+* High accuracy: Achieves 84.4% Top-1 accuracy on ImageNet-1k (RAVLT-S).
+* Parameter efficiency: Uses only 26M parameters (RAVLT-S).
+* Computational efficiency: Achieves 4.6G FLOPs (RAVLT-S).
+* Linear complexity.
+
+RAVLT is based on Rank-Augmented Linear Attention (RALA), a novel attention mechanism that addresses the low-rank limitations of standard linear attention.
+
+
+## Model Variants
+
+Several RAVLT variants were trained, offering different tradeoffs between accuracy, parameters, and FLOPs:
+
+| Model    | Params (M) | FLOPs (G) | Checkpoint     |
+| -------- | ---------- | --------- | --------------- |
+| RAVLT-T  | 15          | 2.4       | [RAVLT-T](https://huggingface.co/aldjalkdf/RAVLT/blob/main/RAVLT_T.pth) |
+| RAVLT-S  | 26          | 4.6       | [RAVLT-S](https://huggingface.co/aldjalkdf/RAVLT/blob/main/RAVLT_S.pth) |
+| RAVLT-B  | 48          | 9.9       | [RAVLT-B](https://huggingface.co/aldjalkdf/RAVLT/blob/main/RAVLT_B.pth) |
+| RAVLT-L  | 95          | 16.0      | [RAVLT-L](https://huggingface.co/aldjalkdf/RAVLT/blob/main/RAVLT_L.pth) |
+
+**Note:**  Accuracy values from the paper have not been transcribed, but should be added once the code is available and the accuracy can be independently verified.
+
+## How to use (Placeholder - Awaiting Code Release)
+
+Instructions on how to use the model will be provided once the code repository is available.  Code will be available at https://github.com/qhfan/RALA.
+
+## Citation
+
+```bibtex
+@misc{fan2024breakinglowrank,
+      title={Breaking the Low-Rank Dilemma of Linear Attention},
+      author={Fan, Qinghao and Liu, Zheng and Li, Hongsheng and Yang, Yisen and Li, Hang},
+      year={2024},
+      eprint={2411.07635},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2411.07635},
+}
+```