Add model card for Parcae-large-770m

This PR adds a comprehensive model card for the Parcae-large-770m model.
- It includes the `pipeline_tag: text-generation` metadata.
- It provides links to the paper "[Parcae: Scaling Laws For Stable Looped Language Models](https://arxiv.org/abs/2604.12946)", the official GitHub repository, and the project page.
- It includes installation and sample usage instructions based on the official repository.
- It provides architectural details for the 770M variant.

README.md

@@ -0,0 +1,52 @@
+---
+pipeline_tag: text-generation
+---
+
+# Parcae-Large-770M
+
+Parcae is a novel stable, looped architecture that addresses instability issues in prior looped language models by recasting looping as a nonlinear time-variant dynamical system over the residual stream. This specific checkpoint is the 770M parameter version, trained on the FineWeb-Edu dataset.
+
+- **Paper:** [Parcae: Scaling Laws For Stable Looped Language Models](https://arxiv.org/abs/2604.12946)
+- **Repository:** [https://github.com/sandyresearch/parcae](https://github.com/sandyresearch/parcae)
+- **Project Page:** [https://sandyresearch.github.io/parcae/](https://sandyresearch.github.io/parcae/)
+
+## Installation
+
+To use this model, you can install the official package:
+
+```bash
+pip install parcae-lm
+```
+
+## Usage
+
+You can load the pretrained weights and instantiate the model using the following code:
+
+```python
+import parcae_lm
+
+# Load the pretrained model from HuggingFace
+model = parcae_lm.from_pretrained("SandyResearch/parcae-large-770m")
+```
+
+## Model Details
+
+This model variant has the following configuration:
+
+| Model | Parameters | Prelude Layers | Core Layers | Coda Layers | Model Dim | Default Recurrence |
+|-------|-----------|---------|------|------|-----------|------------|
+| Parcae-770M | 770M | 6 | 6 | 6 | 1280 | 8 |
+
+## Citation
+
+```bibtex
+@misc{prairie2026parcaescalinglawsstable,
+      title={Parcae: Scaling Laws For Stable Looped Language Models}, 
+      author={Hayden Prairie and Zachary Novack and Taylor Berg-Kirkpatrick and Daniel Y. Fu},
+      year={2026},
+      eprint={2604.12946},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2604.12946}, 
+}
+```