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.

Join the Slack!

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

Citations and References

Research:

Reference Implementations:

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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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