pyvene-interventionslisted

# 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 - Share/reproduce intervention experiments via HuggingFace - Work with any PyTorch architecture (not just transformers) **Consider alternatives when:** - You need exploratory activation analysis → Use **TransformerLens** - You want to train/analyze SAEs → Use **SAELens** - You need remote execution on massive models → Use **nnsight** - You want lower-level control → Use **nnsight** ## Installation ```bash pip install pyvene ``` Standard import: ```python import pyvene as pv ``` ## Core Concepts ### IntervenableModel The main class that wraps any PyTorch model with intervention capabilities: ```python import pyvene as pv from transformers import AutoModelForCausalLM, Auto