transformer-lens-interpretability

What problem does it solve?

This Skill provides the tools and guidance to deeply analyze the internal workings of transformer models, enabling researchers to understand how they learn and process information.

Core Features & Use Cases

• Mechanistic Interpretability: Reverse-engineer algorithms, study attention patterns, and analyze circuits within transformer models.
• Activation Patching: Perform causal tracing experiments to identify the impact of specific activations on model outputs.
• Use Case: A researcher wants to understand why a language model makes a specific prediction. They can use this Skill to isolate and analyze the attention heads and neuron activations responsible for that prediction.

Quick Start

Use the transformer-lens-interpretability skill to perform activation patching experiments on a GPT-2 model.