decoderesearch/SAELens

Health v2 · maintenance only

Adoption not part of the score

1510 stars · 263 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SAELens is a Python library for training sparse autoencoders (SAEs) on language model activations and analyzing them for mechanistic interpretability research. It supports loading pre-trained SAEs, training custom ones, and generating feature dashboards, working with any PyTorch-based model.

Use cases

• train sparse autoencoders on language model activations
• analyze SAE features for mechanistic interpretability research
• download and load pre-trained sparse autoencoders
• generate feature dashboards for SAE analysis
• interpret what neurons/features do inside an LLM
• research AI safety and alignment with interpretability tools

When to choose

• you want to train or analyze sparse autoencoders on transformer models
• you're doing mechanistic interpretability research on language models
• you need pre-trained SAEs for models like those in TransformerLens or Hugging Face
• you want to visualize SAE features with dashboards

When to avoid

• you need general-purpose model training rather than SAE-specific tooling
• you're not working with PyTorch-based models
• you want a no-code interpretability tool rather than a Python library

Facets

library · maturity active

machine-learning deep-learning llm-training data-visualization sdk machine-learning deep-learning large-language-models artificial-intelligence python cross-platform sparse-autoencoders mechanistic-interpretability interpretability transformerlens pytorch ai-safety feature-dashboards research gpu

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