---
pipeline_tag: text-generation
---

# Parcae: Scaling Laws For Stable Looped Language Models

[**Paper**](https://huggingface.co/papers/2604.12946) | [**Project Page**](https://sandyresearch.github.io/parcae/) | [**GitHub**](https://github.com/sandyresearch/parcae)

Parcae is a novel stable, looped architecture for language models. Unlike traditional fixed-depth architectures that scale by increasing parameter count, looped architectures increase compute (FLOPs) by sending activations through a block of layers in a loop. Parcae addresses training instabilities in prior looped models by recasting looping as a nonlinear time-variant dynamical system and constraining the spectral norm of injection parameters.

This checkpoint is the 140M parameter version of Parcae trained on the FineWeb-Edu dataset.

## Installation

To use this model, install the `parcae-lm` package:

```bash
pip install parcae-lm
```

## Usage

You can load the pretrained weights using the following code:

```python
import parcae_lm

# Load a pretrained model from HuggingFace
model = parcae_lm.from_pretrained("SandyResearch/parcae-small-140m")
```

## 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}, 
}
```