SKILL
mechanistic-interpretability-saelens
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
Target: Learning & research. Automatically collected · unverified.
Use with Cursor
A guide for this combination is not available yet, and compatibility has not been verified.
Requirements
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