Octopus V2

Octopus V2, and its subsequent iterations, represent a line of research focused on developing efficient and effective on-device language models, particularly for AI agents and multimodal applications. Current work emphasizes improving model accuracy and latency, often through techniques like hierarchical clustering for long sequence processing and the integration of multiple specialized models via functional tokens. This research aims to overcome limitations of large, cloud-based models by creating smaller, faster, and more privacy-preserving alternatives suitable for deployment on resource-constrained devices, thereby expanding the accessibility and applicability of AI.