HyperET: Efficient Training in Hyperbolic Space for Multi-modal Large Language Models

The recent paper on HyperET presents a groundbreaking approach to training multi-modal large language models (MLLMs) more efficiently in hyperbolic space. This innovation addresses the significant computational demands typically associated with MLLMs, which often require thousands of GPUs for effective training. By focusing on the inefficiencies in existing vision encoders like CLIP and SAM, the authors propose a method that could enhance cross-modal alignment, making it easier and more accessible for researchers and developers to leverage these powerful models. This advancement is crucial as it could lead to faster development cycles and broader applications of AI technologies.