Microsoft releases Harrier-OSS, a family of multilingual text embedding models in three sizes, achieving state-of-the-art results on MTEB v2 benchmark.

Harrier-OSS-v1 comes in three model sizes: 27B, 0.6B, and 270M parameters

Uses decoder-only architecture with last-token pooling and L2 normalization for dense text embeddings

Achieves state-of-the-art performance on Multilingual MTEB v2 benchmark

Applicable to retrieval, clustering, semantic similarity, classification, bitext mining, and reranking tasks

All models are available on Hugging Face and open-source