Artificial Intelligence AI Infrastructure Technology AI Shorts Applications Editors Pick Language Model Embedding Model Large Language Model Machine Learning New Releases Software Engineering Staff Tech News

Liquid AI Introduces LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M: Dense Bi-Encoder and Late-Interaction Models for Fast Multilingual Search Across 11 Languages

By Asif Razzaq - June 19, 2026

This week, Liquid AI released two new retrieval models. They are LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M . Both hold 350M parameters. Both are the first bidirectional members of the LFM family. They build on LFM2.5-350M-Base , released in March. The pair targets fast multilingual and cross-lingual search across 11 languages. Their footprint is small enough to run almost anywhere. Both are available now on Hugging Face under the LFM Open License v1.0.

LFM2.5 Retrievers

The two models share one backbone but represent text differently. LFM2.5-Embedding-350M is a dense bi-encoder. It turns each document into a single vector. Pick it when you want the fastest search and the smallest, cheapest index.

LFM2.5-ColBERT-350M is a late-interaction model. It converts each token into a vector rather than one vector per document. This lets it match queries word-by-word for higher accuracy and better generalization. The trade-off is a larger index. Pick it when accuracy matters more than storage. Its query length is capped at 32 tokens. It can also rerank a first-stage retriever’s results without building an index.

Both target short-context search. Good fits include product catalogs, FAQ knowledge bases, and support docs. Liquid AI positions both as a drop-in replacement for an existing RAG pipeline.

The Architecture Change: Causal to Bidirectional

Both models start from LFM2.5-350M-Base, a mid-trained general-purpose checkpoint. Liquid AI applies a small set of bidirectional patches to the LFM2 architecture. These adapt it from a causal decoder to a bidirectional encoder.