TransformerLens Mechanistic Interpretability

About

This skill empowers researchers and developers to reverse-engineer transformer models using TransformerLens, the industry-standard library for mechanistic interpretability. It provides structured guidance and implementation patterns for complex tasks like activation patching, causal tracing, and circuit analysis. By leveraging HookedTransformer interfaces, users can perform deep-dive investigations into model internals, identify induction heads, and conduct direct logit attribution to understand the underlying algorithms learned during model training.

Key Features

• Native support for 50+ model families including GPT-2, LLaMA, and Mistral
• Comprehensive workflows for activation patching and causal tracing experiments
• 384 GitHub stars
• Structured patterns for circuit analysis, including IOI and induction heads
• Fine-grained activation access through HookedTransformer and HookPoints
• Efficient activation caching strategies for optimized memory management

Use Cases

• Identifying causal components within a model that contribute to specific logic or biases
• Reverse-engineering specific model behaviors to understand internal information flow
• Analyzing attention patterns and residual streams to map model circuits