# Liquid AI — LFM2.5 Retrievers: Bi-directional LFMs for Fast Multilingual Search

- Company: Liquid AI (liquid.ai)
- Announced: 2026-06-18
- Category: not stated
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.liquid.ai/blog/lfm2-5-retrievers
- Record: https://forck.live/items/16782-lfm2-5-retrievers-bi-directional-lfms-for-fast-multilingual-search
- Subject: LFM / d1 models

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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Record: https://forck.live/items/16782-lfm2-5-retrievers-bi-directional-lfms-for-fast-multilingual-search
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