pyvene: A Library for Understanding and Improving PyTorch Models via
  Interventions
Zhengxuan Wu Atticus Geiger Aryaman Arora Jing Huang Zheng Wang Noah D. Goodman Christopher D. Manning Christopher Potts
Interventions on model-internal states are fundamental operations in many
areas of AI, including model editing, steering, robustness, and
interpretability. To facilitate such research, we introduce $\textbf{pyvene}$,
an open-source Python library that supports customizable interventions on a
range of different PyTorch modules. $\textbf{pyvene}$ supports complex
intervention schemes with an intuitive configuration format, and its
interventions can be static or include trainable parameters. We show how
$\textbf{pyvene}$ provides a unified and extensible framework for performing
interventions on neural models and sharing the intervened upon models with
others. We illustrate the power of the library via interpretability analyses
using causal abstraction and knowledge localization. We publish our library
through Python Package Index (PyPI) and provide code, documentation, and
tutorials at https://github.com/stanfordnlp/pyvene.