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transformer-lens-interpretability

Guide mechanistic interpretability research on transformer models using TransformerLens.

Updated Apr 3, 2026
Search Tags:#mechanistic-interpretability#activation-patching#circuit-analysis#transformer-lens#induction-heads#logit-lens
One-click install
npx skills add https://github.com/handsomelong922/my-codex-skills --skill transformer-lens-interpretability-handsomelong922
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: transformer-lens-interpretability
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/transformer-lens
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill transformer-lens-interpretability-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

TransformerLens provides a structured framework and workflows to perform mechanistic interpretability research on transformer models by exposing hook points and activation caches for inspection and controlled manipulation.

Core Features & Use Cases

  • Activation caching and HookPoints: collect and inspect intermediate activations to analyze model behavior.
  • Activation patching and causal tracing: test causal effects by swapping activations during forward passes.
  • Circuit and induction-head analysis: study information flow and identify functional subnetworks such as induction heads.
  • Layer-wise interpretation workflows: layer-by-layer attribution, logit lens projections, and attention pattern analysis.
  • Reference materials and tutorials: integrated references and tutorials to guide experiments.

Quick Start

Install TransformerLens and load a pretrained GPT-2 model to start caching activations and exploring hook points.

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 to trace causal circuits in transformer models?▼

Activation patching traces causal circuits by swapping intermediate activations during forward passes. This Skill guides researchers through caching hook points and patching activations to test causal effects on model behavior.

What is the best way to analyze induction heads in GPT-2?▼

Analyzing induction heads in GPT-2 involves inspecting attention patterns and activation caches. This Skill provides workflows to identify functional subnetworks and study information flow using TransformerLens hook points.

How does logit lens projection work for layer-wise interpretation of LLaMA architectures?▼

Logit lens projection interprets LLaMA architectures by projecting intermediate layer activations onto the output vocabulary. This Skill enables layer-by-layer attribution to observe how predictions form across model depth.

Do I need a specific Python environment to use TransformerLens for circuit analysis?▼

Circuit analysis requires a Python environment with TransformerLens installed, HuggingFace access to pretrained models, and compatible hardware to run forward passes and cache activations.

Can I use TransformerLens for IOI circuit discovery across different model architectures?▼

TransformerLens supports IOI circuit discovery across GPT-2, LLaMA, and related architectures. This Skill applies mechanistic interpretability workflows to expose hook points for controlled manipulation and analysis.

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