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transformer-lens-interpretability

Inspect and manipulate transformer model internals using TransformerLens HookPoints.

11.5k|842|Updated Nov 3, 2025

Search Tags:#deep learning #ai research #mechanistic interpretability #activation patching #transformerlens #circuit analysis

Ready to run

npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill transformer-lens-interpretability

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: transformer-lens-interpretability
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/04-mechanistic-interpretability/transformer-lens
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill transformer-lens-interpretability

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformer-lens, torch, and includes scripts (resource) and references (resource) components.

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.

Frequently Asked Questions about transformer-lens-interpretability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is mechanistic interpretability and how does it apply to transformer models?▼

Mechanistic interpretability reverse-engineers algorithms within transformer models by isolating attention patterns and neuron activations. TransformerLens enables this analysis via HookPoints and activation caching to understand how models process information and make predictions.

How do I perform activation patching experiments on a GPT-2 model?▼

Activation patching identifies the causal impact of specific activations on model outputs. You can perform causal tracing experiments on GPT-2 using TransformerLens by caching activations, patching them via HookPoints, and measuring the resulting output changes.

Do I need PyTorch installed to use TransformerLens for circuit analysis?▼

Yes, PyTorch and TransformerLens are required dependencies for circuit analysis. The deterministic analysis of transformer architectures relies on the Torch backend to run model inference and manipulate internal activations.

How can I inspect and manipulate attention heads in a transformer network?▼

You can inspect and manipulate attention heads using TransformerLens HookPoints. By caching activations during model inference, you can analyze attention patterns and neuron activations to reverse-engineer the specific circuits responsible for predictions.

What's the best way to study why a language model makes a specific prediction?▼

The best way to study transformer predictions is mechanistic interpretability using TransformerLens. You can isolate specific attention heads and neuron activations through activation patching and circuit analysis to trace the prediction's causal pathway.