transformer-lens-interpretability

Name: transformer-lens-interpretability
Availability: InStock
Author: KappTech88

Expose transformer activations and hook points for mechanistic interpretability.

Updated Mar 30, 2026

Search Tags:#mechanistic-interpretability #activation-patching #causal-tracing #transformer-lens #hookpoints #logit-attribution #induction-heads

Ready to run

npx skills add https://github.com/KappTech88/AI-RESEARCH-SKILLS-MCP --skill transformer-lens-interpretability-kapptech88

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: transformer-lens-interpretability
Source: https://github.com/KappTech88/AI-RESEARCH-SKILLS-MCP/tree/main/skills/transformer-lens
Command: npx skills add https://github.com/KappTech88/AI-RESEARCH-SKILLS-MCP --skill transformer-lens-interpretability-kapptech88

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

TransformerLens provides mechanistic interpretability tooling to inspect transformer activations and hooks for causal analysis.

Core Features & Use Cases

Activation caching and patching for causal tracing
Induction head and circuit analysis with per-layer attention data
Logit lens and attribution workflows for model outputs

Quick Start

Load a GPT-2 small model with HookedTransformer.from_pretrained and run_with_cache on a sample prompt to observe residuals and attention patterns.

Frequently Asked Questions about transformer-lens-interpretability

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

FAQPage Schema

How do I run activation patching for mechanistic interpretability on GPT-2?▼

Activation patching for mechanistic interpretability uses TransformerLens HookedTransformer APIs to load GPT-2 and run_with_cache, enabling causal tracing by swapping cached activations into specific hook points during inference.

What is TransformerLens used for in transformer model research?▼

TransformerLens is used for mechanistic interpretability research, exposing transformer activations and hook points to perform causal analysis, circuit discovery, and logit attribution across models like GPT-2.

How do I find induction heads and analyze attention circuits in a transformer?▼

Finding induction heads involves using TransformerLens to extract per-layer attention data from the activation cache, allowing you to identify and analyze specific circuit behaviors within the transformer model architecture.

Can I apply logit lens and attribution workflows to trace model outputs?▼

Logit lens and attribution workflows can be applied to trace model outputs by accessing residual streams and attention patterns cached via the HookedTransformer API during the forward pass.

Do I need PyTorch to inspect transformer internals with HookedTransformer?▼

PyTorch is required alongside TransformerLens to inspect transformer internals, as HookedTransformer relies on PyTorch tensors to expose model activations and execute hook interventions for causal analysis.