A compact multilingual workhorse focused entirely on turning text into dense vector representations. It handles multiple languages without requiring separate models for each, making cross-lingual semantic search and similarity tasks straightforward. Its 278M parameter size keeps it deployable in resource-constrained environments where larger embedding models would be impractical.