Preliminary results from the work of Paras Parani, Dr. Umair Mohammad, and Dr. Fahad Saeed on lightweight seizure prediction models were accepted at the 2024 IEEE International Conference on Big Data workshop HPC-BOD. This research introduces ESPFormer, a lightweight transformer-based model, designed to predict epileptic seizures using patient-independent EEG data. By tokenizing and adapting multi-channel EEG time-series data, the model achieves a balance between accuracy and computational efficiency, enabling near real-time prediction capabilities. The proposed architecture outperforms resource-intensive models like Vision Transformers (ViTs) and Large Language Models (LLMs) on several benchmarks. Successful implementation of ESPFormer marks a significant step forward in developing scalable and effective seizure prediction technologies.