Sparse Autoencoder Training
Skill Verified ActiveProvides 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.
To enable researchers and practitioners to decompose neural network activations into interpretable features using Sparse Autoencoders, facilitating a deeper understanding of model internals.
Features
- Train Sparse Autoencoders (SAEs)
- Analyze pre-trained SAEs and features
- Perform feature attribution and steering
- Decompose neural network activations
- Discover interpretable features
Use Cases
- Discovering interpretable features in model activations
- Studying superposition and feature representation
- Performing feature-based analysis for model understanding
- Analyzing safety-relevant features in language models
Non-Goals
- Replacing general neural network analysis tools
- Providing causal intervention experiments (use TransformerLens directly)
- Production deployment steering (consider direct activation engineering)
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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