LFM2.5-Embedding-350M & LFM2.5-ColBERT-350M

LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M have been released, offering state-of-the-art multilingual retrieval capabilities. The LFM2.5-Embedding-350M model is a dense bi-encoder that produces a single vector per document for efficient cross-lingual search across 11 languages, while LFM2.5-ColBERT-350M employs a late interaction approach with token-level vector storage and MaxSim matching for high-accuracy retrieval. Both models demonstrate inference speeds comparable to smaller models and can be integrated into existing RAG pipelines, making them valuable for practitioners seeking to enhance multilingual retrieval performance.