Studies train-time knowledge injection via hypernetworks that generate fixed LoRA adapters from large fact corpora, empirically characterizing power-law scaling across hypernetwork depth, width, and target model size and reporting improved OOD generalization.
AI Train2017
Trains vision, text, tabular, recommendation, and medical imaging models through a layered PyTorch API. High-level learners cover common workflows, while lower layers stay available when researchers need custom behavior.