Training and Analyzing Sparse Autoencoders (SAEs)

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  • Author: Joseph Bloom
  • Requires: Python <4.0, >=3.10
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Project description

Screenshot 2024-03-21 at 3 08 28 pm

SAE Lens

PyPI License: MIT build Deploy Docs codecov

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 humanities 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 Channin.

Join the Slack!

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

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Research:

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Avatar for chanind from gravatar.com chanind Avatar for jbloomaus from gravatar.com jbloomaus

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  • Author: Joseph Bloom
  • Requires: Python <4.0, >=3.10
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6.38.0

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