transformer-lens-interpretability

Inspect and manipulate transformer model internals with TransformerLens.

3|Updated Feb 21, 2026

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

Ready to run

npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill transformer-lens-interpretability-ihatesea69

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: transformer-lens-interpretability
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/04-mechanistic-interpretability/transformer-lens
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill transformer-lens-interpretability-ihatesea69

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 perform mechanistic interpretability research on transformer models, enabling a deep understanding of how they learn and operate.

Core Features & Use Cases

Activation Patching: Understand causal relationships between model activations and outputs.
Circuit Analysis: Identify and analyze specific computational circuits within transformers (e.g., induction heads).
Use Case: When investigating why a language model makes a specific prediction, use this Skill to trace the activations through layers and heads to pinpoint the exact components responsible for that output.

Quick Start

Use the transformer-lens-interpretability skill to perform activation patching experiments on the gpt2-small model.

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 on transformer models?▼

Activation patching on transformer models is executed by manipulating internal activations to identify causal relationships between specific components and outputs. This Skill provides scripts to trace and patch activations directly within TransformerLens.

What is mechanistic interpretability in deep learning?▼

Mechanistic interpretability in deep learning is the process of reverse-engineering neural network algorithms to understand internal circuits and attention patterns. This Skill enables detailed inspection and manipulation of transformer model components.

Can I analyze induction heads and circuit behavior with TransformerLens?▼

Yes, you can analyze induction heads and circuit behavior with TransformerLens. This Skill enables detailed circuit analysis to identify and inspect specific computational components within transformer architectures.

Do I need PyTorch installed to run circuit analysis experiments?▼

Yes, PyTorch is required to run circuit analysis experiments. Both TransformerLens and PyTorch are necessary dependencies for analyzing transformer architectures and executing mechanistic interpretability tasks.

Why does a language model make a specific prediction?▼

A language model makes a specific prediction due to internal computational circuits processing activations through layers. This Skill helps trace those activations to pinpoint the exact transformer components responsible for a specific output.