Try LFM • Docs • LEAP • Discord LFM2.5 ColBERT 350M We release two new best in class multilingual retrieval models: LFM2.5 Embedding 350M — A dense bi encoder, one vector per document. Smallest, fastest index. LFM2.5 ColBERT 350M — A late interaction model. One vector per token , matched via MaxSim. Higher accuracy and better generalization at the cost of index size. Both models are 350M params and the first bidirectional members of the LFM family, built on LFM2.5 350M Base. They can be used as a drop in replacement for your current RAG pipeline and target fast, cheap, and reliable multilingual / cross lingual search across 11 languages. Find more details about the bidirectional architecture and training recipe in our blog post. [!NOTE] 💻 Demo : https://huggingface.co/spaces/LiquidAI/colbert tool selection 📄 Model details Property LFM2.5 ColBERT 350M LFM2.5 Embedding 350M Type Late interaction (per token vectors) Dense bi encoder (single vector) Total parameters ~353M ~354M Backbone LFM2.5 350M Base + bi directional patches LFM2.5 350M Base + bi directional patches Layers 17 (10 conv + 6 attn + 1 dense) 17 (10 conv + 6 attn + 1 pool) Vocabulary size 64,402 65,536 Output 128 dim p…
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