In this work we investigate the implementation of Graphormer to create the first method-independent predictive model of RTs for reverse phase (RP) and hydrophobic interaction liquid chromatography (HILIC). Graphormer was developed by Ying et al. while working at Microsoft in 2021, and it extends the transformer architecture to graph neural networks. This architecture won the 2021 Open Catalyst challenge by utilizing a global receptive field and attention mechanisms that allow for highly contextual descriptions. Graphormer has previously achieved state-ofthe-art predictions across a wide variety of (bio)cheminformatics tasks. We utilize empirical descriptions of chromatographic metadata from RepoRT that are passed to a flexible "pre-graph" chromatography encoder that derives a dense, learned representation of method data. This information is stored in a global graph node that allows method information to be considered in context of the molecular structure. We explore the strengths of Graphormer-RT in predictions for different methods (i.e., different columns, gradients, manufacturers, etc.), and further assess model generalizability by excluding methods from the training library and testing model performance against the held-out methods. We also show that these formalisms can be extended to HILIC RTs. To the best of our knowledge, this work achieves the first method-independent RT predictions, as well as the first prediction of HILIC RTs for small molecules.