Finally, we used the new electronic structure data generated in this work to train multiple machine learning models that can predict MOF band gaps at various levels of theory from graphs of the underlying crystal structures. We find that individual graph neural network models can predict PBE, HLE17, HSE06 * or HSE06 band gaps from the QMOF Database with a testing-set mean absolute error of 0.23 -0.29 eV. A multi-task graph neural network model capable of simultaneously predicting MOF band gaps for all four functionals performs slightly better than the individual models, but with three or more functionals to train on, a multi-fidelity model achieves the best performance of the models tested in this work.