From the source
Today, we’re releasing two new multilingual retrieval models: LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M .
Both are 350M-parameter models and the first bidirectional members of the LFM family, building on our LFM2.5-350M-Base from March.
They are built for fast and reliable multilingual and cross-lingual search across 11 languages, with a footprint small enough to run almost anywhere.
They are especially well-suited for short-context search: product catalogs, FAQ knowledge bases, support docs, and other collections that need to be searched quickly, cost-effectively, and reliably across languages.
The two models suit different needs: LFM2.5-Embedding-350M turns each document into a single vector.
Pick it when you want the fastest search and the smallest, cheapest index.
LFM2.5-ColBERT-350M converts each token into a vector rather than a single vector per document.
This lets it match queries word-by-word for higher accuracy and better generalization, at the cost of a larger index.
Pick it when accuracy matters more than storage.
Architecture Updates Both models are built from LFM2.5-350M-Base , a mid-trained general-purpose checkpoint.
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