The Model Openness Framework (MOF) defines a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. The MOF aims to prevent misrepresentation of models claiming to be open, to guide researchers and developers in providing all model components under permissive licenses, and to help individuals and organizations differentiate models that are truly open from those that are not. This session will give attendees an overview of the MOF and practical information on how they can use it along with the Model Openness Tool (MOT) to find out which models are really open and evaluate how their own models line up with the MOF.