Understanding and manipulating neural models is essential in the evolving field of AI. This necessity stems from various applications, from refining models for enhanced robustness to unraveling their decision-making processes for greater interpretability. Amidst this backdrop, the Stanford University research team has introduced 'pyvene,' a groundbreaking open-source Python library that facilitates intricate interventions on PyTorch models. pyvene is ingeniously designed to overcome the limitations posed by existing tools, which often need more flexibility, extensibility, and user-friendliness. At the heart of pyvene's innovation is its configuration-based approach to interventions. This method departs from traditional, code-executed interventions, offering a more intuitive and