This study demonstrates the application of graph transformers in predictive method-independent models of RP and HILIC chromatography. After filtering the RepoRT dataset to improve method standardization, we develop formalisms to describe the gradient and column metadata for machine learned graph embeddings. Along with the chromatographic method information, we employed SMILES strings as input. The final RP dataset contained 142,688 RTs associated with 191 distinct methods and the final HILIC dataset contained 4,373 RTs associated with 49 methods. A key addition was a set of "pre-graph" encoding neural networks designed to create a learned representation of gradient and column information. This information was fed into a flexible global node that was connected to all other nodes (viz. atoms) in the graph via a special edge type. This feature ensures that Graphormer-RT considers the column and gradient information in the context of molecular structure. Our best performing RP prediction method achieved a MAE = 29.3 ± 0.6 s, a meaningful improvement over previous state-of-the-art models, which employ only a single chromatographic method for training (compared to 191 here). Our best performing HILIC model achieved MAE = 42.4 ± 2.9 s. To the best of our knowledge, Graphormer-RT is the first "method-independent" prediction of RP and HILIC RTs.