LEAF: Distillation of State‑of‑the‑Art Text Embedding Models

MongoDB Research introduces the Lightweight Embedding Alignment Framework (LEAF), a novel knowledge distillation framework aimed at producing smaller, faster, and more flexible text embedding models that are interoperable with larger teacher models. LEAF facilitates the creation of compact models that maintain compatibility with the teacher models' embedding spaces, thus enabling efficient deployment on CPU-only and mobile devices without requiring internet connectivity. Two models, mdbr-leaf-ir and mdbr-leaf-mt, have been released under the Apache 2.0 license, optimized for information retrieval tasks and general NLP applications respectively, and have shown state-of-the-art performance on public leaderboards for their size category. These models, which run efficiently on modest hardware, are well-suited for scenarios where GPUs are unavailable, and they offer significant advantages in terms of speed and resource requirements. By allowing flexible asymmetric architectures and requiring less training data, LEAF models demonstrate robust performance while supporting fine-tuning for domain-specific tasks, making them valuable tools for modern AI-enabled applications.

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