# LEAF (Lightweight Embedding Alignment Framework)

MongoDB Research has released LEAF, a knowledge distillation framework, together with two 23M-parameter text embedding models, mdbr-leaf-ir and mdbr-leaf-mt. State-of-the-art embedding models have grown to hundreds of millions or billions of parameters, typically need GPU servers, and are not compatible with one another, so swapping models means recomputing every document embedding. LEAF aligns only the teacher and student output embeddings; the distilled models retain up to 97% of their teacher's performance while being 5–15× smaller and 7–24× faster, and can run offline on CPU-only servers, IoT and mobile devices.

MongoDB Research has released LEAF, a knowledge distillation framework, together with two 23M-parameter text embedding models, mdbr-leaf-ir and mdbr-leaf-mt. State-of-the-art embedding models have grown to hundreds of millions or billions of parameters, typically need GPU servers, and are not compatible with one another, so swapping models means recomputing every document embedding. LEAF aligns only the teacher and student output embeddings; the distilled models retain up to 97% of their teacher's performance while being 5–15× smaller and 7–24× faster, and can run offline on CPU-only servers, IoT and mobile devices.