AI BriefingLiquid AI Releases Multilingual Embedding Models for RAG
Key point
Liquid AI has released highly efficient multilingual embedding and ColBERT models to improve RAG performance.
Details
Liquid AI has released LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M. Both models are based on the LFM2 backbone, delivering very fast inference speed despite their small size.
LFM2.5-Embedding-350M is a highly efficient dense bi-encoder supporting 11 languages, generating a single vector per document to minimize index size while providing high multilingual retrieval accuracy.
LFM2.5-ColBERT-350M is a late interaction retriever that stores vectors at the token level. This allows it to maintain very high accuracy even when searching documents written in English using other languages, and it can be dropped into existing RAG pipelines as an immediate replacement to improve performance.
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