transformer-lens-interpretability

Activation patching on transformer models is facilitated through TransformerLens by caching activations and manipulating model internals via HookPoints. This allows you to perform causal tracing experiments to identify influential activations and reverse-engineer learned algorithms.

Mechanistic interpretability analyzes transformer internals by inspecting attention patterns and emergent circuits using activation caching. It enables researchers to isolate specific attention head contributions to understand their role in linguistic phenomena.

TransformerLens supports circuit analysis for a wide range of transformer architectures, including GPT-2, LLaMA, and Mistral. This allows you to study attention patterns and induction heads across different model types using a consistent workflow.

Inspecting transformer internals involves using HookPoints in TransformerLens to access and manipulate cached activations during processing. This workflow enables you to isolate components and reverse-engineer learned algorithms to analyze model behavior.

To isolate a specific attention head, use TransformerLens to load a model like gpt2-small, cache its activations for a targeted prompt, and apply activation patching. This causal tracing method identifies the head's contribution to the phenomenon.

Yes, PyTorch is required as a core dependency alongside the TransformerLens library to run mechanistic interpretability workflows. These frameworks provide the foundational environment needed to load models, cache activations, and perform circuit analysis.