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Llama-3.2-1B-sae
seonglae/Llama-3.2-1B-sae
Llama-3.2-1B-sae is a machine learning model from seonglae. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for saelens.
This repository contains the following SAEs: - Llama-3.2-1Bblocks.12.hookresidpre14336topk480.000249fineweb512 - Llama-3.2-1Bblocks.12.hookresidpre14336topk480.000242faithful-llama3.2-1b512 - Llama-3.2-1Bblocks.12.hoo…
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Public
Updated Jul 2, 2025
Repo size
470 MB
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.safetensors470 MB · 100%
At a glance
- Library
- saelens
- Access
- Public
- Created
- Apr 8, 2025
- Updated
- Jul 2, 2025
- SHA
- 257e17b4
Model card
From the Hugging Face model README
SAEs for use with the SAELens library
This repository contains the following SAEs:
- Llama-3.2-1B_blocks.12.hook_resid_pre_14336_topk_48_0.0002_49_fineweb_512
- Llama-3.2-1B_blocks.12.hook_resid_pre_14336_topk_48_0.0002_42_faithful-llama3.2-1b_512
- Llama-3.2-1B_blocks.12.hook_resid_pre_14336_topk_48_0.0002_42_fineweb_512
- Llama-3.2-1B_blocks.12.hook_resid_pre_14336_topk_48_0.0002_49_faithful-llama3.2-1b_512
Load these SAEs using SAELens as below:
from sae_lens import SAE
sae, cfg_dict, sparsity = SAE.from_pretrained("seonglae/Llama-3.2-1B-sae", "<sae_id>")
Citation
@inproceedings{cho2025faithfulsae,
title={Faithful{SAE}: Towards Capturing Faithful Features with Sparse Autoencoders
without External Datasets Dependency},
author={Seonglae Cho and Harryn Oh and Donghyun Lee and Luis Eduardo Rodrigues Vieira
and Andrew Bermingham and Ziad El Sayed},
booktitle={ACL 2025 Student Research Workshop},
year={2025},
url={https://openreview.net/forum?id=tBn9ChHGG9}
}
- Library
- saelens
- Created
- Apr 8, 2025
- Updated
- Jul 2, 2025