In molecular studies, the accurate parametrization of small molecules stands as an essential yet growing demand. Addressing this, we introduce ParametrizANI, a tool crafted explicitly for establishing detailed protocols for dihedral parametrization using GAFF and OpenFF force fields. The robust PyTorch-based program, TorchANI, forms the backbone of ParametrizANI, functioning as a benchmark to uphold precision in parametrization tasks. TorchANI plays a pivotal role in enabling ANI (ANAKIN-ME) deep learning models, which are trained to predict potential energy surfaces and related molecular properties with high accuracy. Our work on ParametrizANI goes beyond just creating a tool; it is about building a research-friendly environment, free from the constraints of limited resources. We are committed to democratizing research, enabling teams of all sizes to perform dihedral parametrization with DFT-level accuracy. This tool opens up new possibilities in molecular dynamics and related fields. It marks a significant step forward in improving the scientific community's ability to parametrize small molecules. For a detailed look at its features, we invite you to check out ParametrizANI on GitHub (https://github.com/palermolab/ParametrizANI).