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mechanistic-interpretability-pyvene

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Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.

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# 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](https://github.com/stanfordnlp/pyvene) (840+ stars)
**Paper**: [pyvene: A Library for Understanding and Improving PyTorch Models via Interventions](https://aclanthology.org/2024.naacl-demo.16) (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
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