transformer-lens-interpretability

Activation patching is performed by overwriting specific internal activations during a forward pass to identify which components causally influence a model's prediction. This Skill provides the necessary tools to execute these causal tracing experiments on transformer models.

Mechanistic interpretability reverse-engineers learned algorithms by analyzing transformer circuits and attention patterns. Circuit analysis inspects activation flows and weights to understand the internal mechanisms driving specific model predictions and behaviors.

Yes, you can inspect attention patterns in models like gpt2-small using TransformerLens. This Skill facilitates detailed inspection and manipulation of internal activations and weights to analyze specific layers and attention heads.

Yes, both TransformerLens and PyTorch are required dependencies for operation. You must have these frameworks installed in your environment to utilize the Skill's tools for inspecting and manipulating transformer internals.

The best way to analyze transformer internals is by using dedicated interpretability tools to inspect and manipulate weights and activations. This approach enables detailed circuit analysis and causal tracing experiments to reverse-engineer learned algorithms.