Base Model

Base models are pre-trained models serving as foundational starting points for further training or adaptation in various machine learning tasks, aiming to improve efficiency and performance. Current research focuses on optimizing base model selection strategies, including exploring the impact of model size, training data, and architecture on downstream performance, as well as developing methods for fusing or re-weighting existing fine-tuned models to create superior base models. This research is significant because it addresses the high computational cost of training from scratch and enables more efficient and effective development of models across diverse applications, from natural language processing to medical image analysis and scientific computing.