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transformer-lens-interpretability

Inspect and causally manipulate GPT-style transformer activations with TransformerLens.

Updated May 2, 2026

Search Tags:#mechanistic interpretability #attention patterns #activation patching #transformerlens #circuit analysis #activation caching

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformer-lens>=2.0.0, torch>=2.0.0, and includes references (resource) components.

What problem does it solve?

This Skill helps you understand and modify how GPT-style transformers work by inspecting and intervening in internal activations using TransformerLens.

Core Features & Use Cases

Activation caching and inspection: Capture residual streams, attention patterns, and per-head representations for any layer and position.
Activation patching / causal tracing: Replace corrupted activations with clean ones to identify which (layer, position) changes drive output differences.
Circuit analysis workflows: Run interpretable experiments for induction heads, indirect object identification, logit attribution, and SAELens integration.

Quick Start

Ask an AI coding agent to run an activation patching experiment in TransformerLens for a clean vs corrupted prompt pair and return the most causally influential layer-position heatmap.

Frequently Asked Questions about transformer-lens-interpretability

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

FAQPage Schema

How do I perform activation patching in TransformerLens to trace causal circuits?▼

You can perform activation patching by caching activations from a clean prompt and intervening at specific HookPoints in TransformerLens. This identifies which layer and position combinations causally drive the output difference for corrupted prompts.

What is mechanistic interpretability and how does activation caching work for GPT-style transformers?▼

Mechanistic interpretability reverse-engineers transformer computations by inspecting cached activations. Activation caching captures residual streams, attention patterns, and per-head representations like resid_post and pattern tensors across layers for deterministic circuit analysis.

How can I analyze attention patterns for induction head detection in PyTorch transformers?▼

You analyze attention patterns by accessing the pattern activation cache keys via TransformerLens HookPoints. This allows you to inspect per-head attention weights across positions to detect induction heads and map circuit behaviors in GPT-style models.

Do I need PyTorch and specific versions to run TransformerLens for circuit analysis?▼

Yes, mechanistic interpretability experiments with TransformerLens require PyTorch version 2.0.0 or higher and TransformerLens version 2.0.0 or higher. These dependencies provide the tensor computation and HookPoints infrastructure for activation patching workflows.

Can I run logit attribution and IOI circuit analysis workflows with this approach?▼

Yes, TransformerLens supports interpretable circuit analysis workflows including indirect object identification and logit attribution. You can trace how specific attention heads and layers contribute to final token predictions by inspecting cached residual streams.

What are the limitations of using HookPoints for activation patching experiments?▼

Activation patching via HookPoints requires deterministic research pipelines and careful alignment of clean and corrupted prompts. Complex circuits may involve interactions across many layers and positions, making causal interpretation challenging without well-controlled experimental prompt pairs.