Improve model card and add metadata

README.md

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-license: mit
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+---
+license: mit
+pipeline_tag: text-generation
+---
+
+# Solve the Loop: Attractor Models for Language and Reasoning
+
+Attractor Models are a family of models that use a backbone module to propose output embeddings, followed by an attractor module that refines them by solving for a fixed point using implicit differentiation. This architecture allows for iterative refinement with constant training memory and adaptive inference-time computation.
+
+[**Project Page**](https://attractor-models.github.io/) | [**Paper (arXiv:2605.12466)**](https://arxiv.org/abs/2605.12466) | [**GitHub**](https://github.com/jacobfa/Attractor)
+
+## Introduction
+Attractor Models offer a promising alternative to feed-forward computation by iteratively refining latent representations. In language modeling, Attractor Models deliver a Pareto improvement over standard Transformers, improving perplexity and downstream accuracy while reducing training cost. This repository contains the **Attractor-370M** model.
+
+## Sample Usage
+
+To use this model, you need to install the `attractor` package from the [official repository](https://github.com/jacobfa/Attractor):
+
+```bash
+git clone https://github.com/jacobfa/Attractor
+cd Attractor
+pip install -e .
+```
+
+Then, you can construct the model in Python:
+
+```python
+from attractor.models.attractor import Attractor, AttractorConfig
+
+# Loading the configuration for the 370M model
+config = AttractorConfig.from_name("attractor-medium-370m")
+model = config.construct_model()
+```
+
+## Citation
+
+```bibtex
+@article{feinashley2026attractor,
+  title={Solve the Loop: Attractor Models for Language and Reasoning},
+  author={Fein-Ashley, Jacob and Rashidinejad, Paria},
+  year={2026},
+  url={https://arxiv.org/abs/2605.12466}
+}
+```