#Embedding Models

The VultronRetriever family of models has been released on HuggingFace, achieving 1 on the MTEB leaderboard with up to 16x smaller index storage and 12x higher throughput compared to previous 9B-class leaders. This breakthrough enables high-quality retrieval and embedding on edge devices like iPhones entirely offline, expanding AI applications to privacy-sensitive and resource-constrained environments while reducing infrastructure costs. The family includes three models: Prime-8B (global 1), Core-4.5B (outperforming models twice its size), and Flash-0.8B (outperforming models up to 5x its size). All models are trained on contamination-free datasets and leverage the Hydra architecture for late interaction retrieval with up to half the memory of comparable models.