sparse-autoencoder-traininglisted

# SAELens: Sparse Autoencoders for Mechanistic Interpretability SAELens is the primary library for training and analyzing Sparse Autoencoders (SAEs) - a technique for decomposing polysemantic neural network activations into sparse, interpretable features. Based on Anthropic's groundbreaking research on monosemanticity. **GitHub**: [jbloomAus/SAELens](https://github.com/jbloomAus/SAELens) (1,100+ stars) ## The Problem: Polysemanticity & Superposition Individual neurons in neural networks are **polysemantic** - they activate in multiple, semantically distinct contexts. This happens because models use **superposition** to represent more features than they have neurons, making interpretability difficult. **SAEs solve this** by decomposing dense activations into sparse, monosemantic features - typically only a small number of features activate for any given input, and each feature corresponds to an interpretable concept. ## When to Use SAELens **Use SAELens when you need to:** - Discover interpretable features in model activations - Understand what concepts a model has learned - Study superposition and feature geometry - Perform feature-based steering or ablation - Analyze safety-relevant features (deception, bias, harmful content) **Consider alternatives when:** - You need basic activation analysis → Use **TransformerLens** directly - You want causal intervention experiments → Use **pyvene** or **TransformerLens** - You need production steering → Consider direct activatio