Microsoft's 270M edge-ready multilingual embedder with 32K context.
A strong 0.268B-parameter dense embedding model from Microsoft. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing.
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The smallest Harrier variant from Microsoft Research, a 268M-parameter Gemma 3-based decoder embedding model designed to run on CPU and edge devices. Trained with contrastive learning plus knowledge distillation from larger teachers, supports 94 languages and 32K-token contexts, with an MIT license enabling commercial use.
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