SAELens exists to help researchers:
- Train sparse autoencoders.
- Analyse sparse autoencoders / research mechanistic interpretability.
- Generate insights which make it easier to create safe and aligned AI systems.
Please refer to the documentation for information on how to:
- Download and Analyse pre-trained sparse autoencoders.
- Train your own sparse autoencoders.
- Generate feature dashboards with the SAE-Vis Library.
SAE Lens is the result of many contributors working collectively to improve humanity's understanding of neural networks, many of whom are motivated by a desire to safeguard humanity from risks posed by artificial intelligence.
This library is maintained by Joseph Bloom and David Chanin.
Pre-trained SAEs for various models can be imported via SAE Lens. See this page in the readme for a list of all SAEs.
- SAE Lens + Neuronpedia
- Loading and Analysing Pre-Trained Sparse Autoencoders
- Understanding SAE Features with the Logit Lens
- Training a Sparse Autoencoder
Feel free to join the Open Source Mechanistic Interpretability Slack for support!
Please cite the package as follows:
@misc{bloom2024saetrainingcodebase,
title = {SAELens},
author = {Joseph Bloom, Curt Tigges and David Chanin},
year = {2024},
howpublished = {\url{https://github.com/jbloomAus/SAELens}},
}