transformer-lens-interpretability

To inspect attention patterns in a transformer model, use TransformerLens to access HookPoints and cache activations. This allows you to visualize specific attention heads and analyze their behavior directly during forward passes.

Activation patching is a mechanistic interpretability technique for causal tracing that overwrites specific cached activations during a forward pass. It identifies which model components and internal circuits are causally responsible for a given prediction.

Yes, you can analyze circuits like induction heads and IOI circuits using TransformerLens. The Skill enables detailed inspection of model internals to reverse-engineer these learned algorithms and understand their specific functions.

Yes, you need PyTorch installed to perform mechanistic interpretability research with this Skill. TransformerLens operates as a PyTorch-based library, requiring both dependencies to load models and manipulate internal activations.

To load gpt2-small and cache activations, use TransformerLens to initialize the model and run a forward pass with your prompt. The HookPoint architecture automatically intercepts and stores intermediate activations for analysis.

The best way to reverse-engineer algorithms learned by a language model is through mechanistic interpretability. By combining activation patching with attention pattern visualization, you can pinpoint the exact circuits driving model behavior.