--- pipeline_tag: text-generation --- # Parcae-xlarge-1.3B Parcae is a novel stable, looped architecture for language modeling. Unlike traditional fixed-depth architectures that scale by increasing parameter counts, Parcae increases FLOPs by sending activations through a block of layers in a loop. It addresses instability issues in prior looped models by recasting looping as a nonlinear time-variant dynamical system and constraining the spectral norm of injection parameters. - **Paper:** [Parcae: Scaling Laws For Stable Looped Language Models](https://huggingface.co/papers/2604.12946) - **Project Page:** [https://sandyresearch.github.io/parcae/](https://sandyresearch.github.io/parcae/) - **Repository:** [https://github.com/sandyresearch/parcae](https://github.com/sandyresearch/parcae) ## Installation To use this model, you can install the `parcae-lm` package: ```bash pip install parcae-lm ``` ## Usage You can load the pretrained weights using the `parcae_lm` library: ```python import parcae_lm # Load this pretrained model from HuggingFace model = parcae_lm.from_pretrained("SandyResearch/parcae-xlarge-1_3b") ``` ## Model Details This specific checkpoint is the 1.3B parameter variant of Parcae, trained on the FineWeb-Edu dataset. | Model | Parameters | Prelude | Core | Coda | Model dim. | Recurrence | |-------|-----------|---------|------|------|-----------|------------| | Parcae-1.3B | 1.3B | 8 | 8 | 8 | 1536 | 8 | **Note:** These are base models without any form of downstream modification (instruction tuning, etc.). ## 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}, } ``` ## References This code-base was built on `karpathy/nanochat`, `seal-rg/recurrent-pretraining`, and `Lightning-AI/litgpt`.