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---
license: apache-2.0
language:
- en
tags:
- mechanistic interpretability
- sparse autoencoder
- llama
- llama-3
---
## Model Information
A SAE (Sparse Autoencoder) for [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
It is trained specifically on layer 9 of Llama 3.2 1B and achieves a final L0 of 63 during training.
This model is used to decompose Llama's activations into interpretable features.
The SAE weights are released under Apache, however Llama 3.2 1B is to be used under Meta's Llama 3.2 License.
## How to use
A Jupyter Notebook is provided to test the model
<a target="_blank" href="https://colab.research.google.com/github/qrsch/SAE/blob/main/SAE.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab", width="200px"/>
</a>
## Training
Our SAE was trained using [LMSYS-Chat-1M dataset](https://arxiv.org/pdf/2309.11998), on a single RTX 3090. The training script will be provided soon in the following repository: https://github.com/qrsch/SAE
## Acknowledgements
This release wouldn't have been possible without the work of [Goodfire](https://www.goodfire.ai/) and [Anthropic](https://transformer-circuits.pub/)
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