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
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 s
Get smart on it
Two new retrieval models have been released that help search across multiple languages by converting text into mathematical representations that computers can compare. One model prioritizes speed and storage efficiency by converting each document into a single vector, while the other trades larger storage for higher accuracy by converting each word into its own vector. Both models are compact enough to run on personal devices and perform better on standardized tests than larger competing models, making them suitable for applications like product catalogs, customer support databases, and enterprise document search.
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