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README.md

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-# <img src="https://i.ibb.co/BC94vq1/intervention-library-icon-1.png" width="50" height="30"> **Customizable Interventions on Internals of Different Neural Architectures**
+# **Customizable Interventions on Internals of Different Neural Architectures**
 To interpret causal mechanisms of neural networks with their internals, we introduce **pyvene**, an open-source and intervention-oriented Python library that supports customizable interventions on different families of neural architectures (e.g., RNN or Transformers). The basic operation is an in-place activation modification during the computation flow of a neural model. It supports complex intervention schemas (e.g., parallel or serialized interventions) and a wide range of intervention modes (e.g., static or trained interventions) to enable practitioners to quantify counterfactual behaviors at scale to gain interpretability insights. We showcase **pyvene** out-of-box supports a wide range of intervention-based interpretability methods such as causal abstraction, circuit finding, and knowledge localization. **pyvene** provides a unified and extensible framework to perform interventions on neural models, and to share interventions with others.