Preliminary results from the work of Paras Parani, Dr. Umair Mohammad and Dr. Fahad Saeed on early seizure prediction using pre-trained large language models (LLMs) and vision transformers (ViTs) were accepted at the 8th International Conference on Data Science and Machine Learning (CDMA 2024). The work proposes techniques to modify multi-channel time-series electroencephalography (EEG) data and pre-trained ViTs and LLMs to predict seizures with the partial re-design and re-training of only the first and last layers. Successful execution of this strategy will be a game-changer for seizure prediction technologies. It will allow for the accurate and precise prediction of seizures using models with billions of parameters that have been trained on huge volumes of data by applying it to retroactive EEG datasets that are comparable much smaller in size.