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README.md

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 - pytorch_model_hub_mixin
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-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+<h1 align="center"><em>EMber</em>: Learning the Shared Latent Structure of Cryo-EM Inverse Problems</h1>
+
+<!-- Replace each href="#" with the author's website URL. -->
+<p align="center">
+  <a href="https://nithinvc.github.io/">Nithin Chalapathi</a> ·
+  <a href="https://www.theosternlieb.com/">Theo Sternlieb</a> ·
+  <a href="#">Ethan Han</a> ·
+  <a href="https://sonyahanson.github.io/">Sonya M. Hanson</a> ·
+  <a href="https://pilarcossio.org/">Pilar Cossio</a> ·
+  <a href="https://a1k12.github.io/">Aditi S. Krishnapriyan</a> ·
+  <a href="https://ritwikgupta.me/">Ritwik Gupta</a>
+</p>
+
+EMber is a family of pretrained vision transformers for extracting representations from single-particle cryo-EM particle images. Given a particle image, EMber produces an image-level embedding and spatial patch features for use in downstream analysis. The models were trained using self-distillation on over 50 million experimental particle images from 214 EMPIAR datasets, together with simulated projections from 268,000 molecular assemblies.
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+Check out our preprint [here](https://arxiv.org/abs/2610.22523) and code [here](https://github.com/nithinvc/EMber).
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+EMber-Small has 21.4M parameters and uses a 16x16 patch size.