Many enterprise search applications require small, efficient embedding models with strong performance on varying downstream tasks. In this paper, we present a pair of new embedding models trained with a novel training regimen that combines model distillation with task-specific contrastive losses. The resulting models are compact, general-purpose text embedding models that match or surpass the performance of existing models of similar size. Furthermore, we study the impact of training using a regularization strategy designed to make embeddings more robust under binary quantization. Our approach yields highly compact models suitable for resource-constrained deployment. All weights are publicly released to foster further research.