transformer-lens-interpretability

SKILL.md

TransformerLens: Mechanistic Interpretability for Transformers

TransformerLens is the de facto standard library for mechanistic interpretability research on GPT-style language models. Created by Neel Nanda and maintained by Bryce Meyer, it provides clean interfaces to inspect and manipulate model internals via HookPoints on every activation.

GitHub: TransformerLensOrg/TransformerLens (2,900+ stars)

When to Use TransformerLens

Use TransformerLens when you need to:

Reverse-engineer algorithms learned during training

Perform activation patching / causal tracing experiments

Study attention patterns and information flow

Analyze circuits (e.g., induction heads, IOI circuit)

Cache and inspect intermediate activations

Apply direct logit attribution