Captum is a cutting-edge open-source library tailored to improve the interpretability of PyTorch models. It equips developers and researchers with a versatile set of tools to dissect and explain the decision-making processes of machine learning models. The library supports a variety of interpretability algorithms, such as attribution methods, feature importance analysis, and layer-wise relevance propagation. These functionalities enable users to pinpoint the most influential features or inputs driving model predictions, facilitating enhanced debugging, validation, and optimization. Captum is particularly valuable in fields where model transparency is paramount, including healthcare, finance, and autonomous systems. Designed with user-friendliness in mind, the library features a straightforward API that integrates effortlessly with PyTorch. Additionally, Captum provides comprehensive documentation and tutorials to help users quickly grasp its capabilities. Whether you're an experienced machine learning expert or a beginner, Captum equips you with the tools to make your models more interpretable and reliable.