| name | saelens |
| description | Use this skill when working with Sparse Autoencoders (SAEs) for mechanistic interpretability of language models, including training SAEs, loading pre-trained SAEs, analyzing neural network features, or integrating SAEs with TransformerLens, HuggingFace Transformers, or other PyTorch-based models. |
SAELens — Sparse Autoencoders for Language Models
When to Use
Activate this skill when:
- Training sparse autoencoders (SAEs) on language model activations
- Loading and analyzing pre-trained SAEs from Neuronpedia or HuggingFace
- Performing mechanistic interpretability research on transformer models
- Investigating neural network features using SAE decompositions
- Generating SAE feature dashboards with SAE-Vis
- Hooking SAEs into transformer forward passes (HookedSAETransformer)
- Evaluating SAE quality (variance explained, L0 sparsity, reconstruction loss)
- Running cache activation pipelines for large-scale SAE training
- Analyzing logit lens features through SAE decomposition
- Working with TopK, Gated, or Standard SAE architectures
Keywords: sparse autoencoder, SAE, mechanistic interpretability, TransformerLens, feature analysis, neural network features, activation patching, SAE training, HookedSAETransformer, Neuronpedia, dictionary learning, GPT-2, language model interpretability
Installation / Setup
Prerequisites
- Python 3.10+
- PyTorch (CUDA recommended for training)
Install from PyPI (recommended)
pip install sae-lens
Install from source (development)
git clone https://github.com/decoderesearch/SAELens.git
cd SAELens
pip install -e ".[dev]"
Quick Reference
Optional dependencies
