SKILL.md
pyvene: Causal Interventions for Neural Networks
pyvene is Stanford NLP's library for performing causal interventions on PyTorch models. It provides a declarative, dict-based framework for activation patching, causal tracing, and interchange intervention training - making intervention experiments reproducible and shareable.
GitHub: stanfordnlp/pyvene (840+ stars) Paper: pyvene: A Library for Understanding and Improving PyTorch Models via Interventions (NAACL 2024)
When to Use pyvene
Use pyvene when you need to:
- Perform causal tracing (ROME-style localization)
- Run activation patching experiments
- Conduct interchange intervention training (IIT)
- Test causal hypotheses about model components
- Share/reproduce intervention experiments via HuggingFace
- Work with any PyTorch architecture (not just transformers)