Add model info

README.md

@@ -1,3 +1,45 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+tags:
+- sparse-autoencoder
+- matryoshka
+- ct
+- mri
+---
+
+  # SAIL — Pretrained SAE Weights
+
+  Pretrained Matryoshka Sparse Autoencoder (SAE) weights for the [SAIL](https://github.com/pwesp/sail) repository. See the project page for the full pipeline and usage instructions.
+
+  Two checkpoints are provided, one for each foundation model (FM) embedding space:
+
+  | File | Foundation model | Input dim | Dictionary sizes | k values |
+  |------|-----------------|-----------|-----------------|----------|
+  | `biomedparse_sae.ckpt` | BiomedParse | 1536 | 128, 512, 2048, 8192 | 20, 40, 80, 160 |
+  | `dinov3_sae.ckpt` | DINOv3 | 1024 | 128, 512, 2048, 8192 | 5, 10, 20, 40 |
+
+  Both SAEs were trained on CT and MRI embeddings from the [TotalSegmentator](https://github.com/wasserth/TotalSegmentator) dataset.
+
+  ## Usage
+
+  To download these weights and place them in the expected directory structure, run from the repo root:
+
+  ```bash
+  bash pretrained/download_weights.sh
+  ```
+
+  ## Citation
+
+  If you find this work useful, please cite our paper:
+
+```bibtex
+  @misc{sail2026,
+    title = {Sparse Autoencoders for Interpretable Medical Image Representation Learning},
+    author = {Wesp, Philipp and Holland, Robbie and Sideri-Lampretsa, Vasiliki and Gatidis, Sergios},
+    year = 2026,
+    journal = {arXiv.org},
+    howpublished = {https://arxiv.org/abs/2603.23794v1}
+  }
+```