SAE Lens

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, Curt Tigges, Anthony Duong and David Chanin.

Loading Pre-trained SAEs.

Pre-trained SAEs for various models can be imported via SAE Lens. See this page in the readme for a list of all SAEs.

Migrating to SAELens v6

The new v6 update is a major refactor to SAELens and changes the way training code is structured. Check out the migration guide for more details.

Tutorials

• SAE Lens + Neuronpedia
• Loading and Analysing Pre-Trained Sparse Autoencoders
• Understanding SAE Features with the Logit Lens
• Training a Sparse Autoencoder

Join the Slack!

Feel free to join the Open Source Mechanistic Interpretability Slack for support!

Citation

Please cite the package as follows:

@misc{bloom2024saetrainingcodebase,
   title = {SAELens},
   author = {Bloom, Joseph and Tigges, Curt and Duong, Anthony and Chanin, David},
   year = {2024},
   howpublished = {\url{https://github.com/jbloomAus/SAELens}},
}