TransformerLens Mechanistic Interpretability
Skill Verifiziert AktivProvides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
To enable researchers to deeply inspect and manipulate the internals of transformer models for understanding their learned algorithms and behavior.
Funktionen
- Inspect and manipulate transformer internals
- Utilize HookPoints and activation caching
- Perform activation patching and causal tracing
- Analyze attention patterns and circuits
- Support for 50+ transformer architectures
Anwendungsfälle
- Reverse-engineering model algorithms
- Studying attention patterns and information flow
- Performing activation patching or causal tracing experiments
- Analyzing specific circuits like induction heads
Nicht-Ziele
- Working with non-transformer architectures
- Training or analyzing Sparse Autoencoders directly
- Providing remote execution on massive models
- Offering higher-level causal intervention abstractions
Praktiken
- Model Interpretability
- Transformer Analysis
- Code Research
Voraussetzungen
- Python >= 3.8
- transformer-lens >= 2.0.0
- torch >= 2.0.0
Trust
- info:Issues AttentionOpen issues (90d): 17, Closed issues (90d): 4. The closure rate is low, indicating slower response times for open issues.
Installation
npx skills add davila7/claude-code-templatesFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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