---
language:
- en
tags:
- causal-lm
library_name: transformers
license: apache-2.0
datasets:
- nvidia/Nemotron-CC-Math-v1
base_model:
- meta-llama/Llama-3.2-1B
---

# Llama-3.2-non-recurrent-posttrained
Llama-3.2-non-recurrent-posttrained is a non-recurrent meta-llama/Llama-3.2-1B based baseline for the [Retrofitting Recurrence](https://hf.co/collections/tomg-group-umd/retrofitting-recurrence) set of models. A set of depth recurrent models trained by taking layers from pretrained feedforward language models ([link to paper](https://arxiv.org/abs/2511.07384)).

## Training
We train using https://github.com/mcleish7/retrofitting-recurrence using AMD MI300A GPUs on [Tuolumne](https://hpc.llnl.gov/hardware/compute-platforms/tuolumne) at Lawrence Livermore National Laboratory.

## Data
Train and validation data is taken from non-overlapping subsets of raw text data. As such it is _not_ an instruction model.

## Licence
This model is released under the [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) licence.

## Contact
Please, feel free to contact us with any questions, or open a discussion thread.

# Citation
```
@article{mcleish2025teaching,
    title={Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence}, 
    author={Sean McLeish and Ang Li and John Kirchenbauer and Dayal Singh Kalra and Brian R. Bartoldson and Bhavya Kailkhura and Avi Schwarzschild and Jonas Geiping and Tom Goldstein and Micah Goldblum},
    journal={arXiv preprint arXiv:2511.07384},
    year={2025}
}
```