Liquid AI drops lightweight multilingual search models
Liquid AI just rolled out two 350M-parameter retrieval models, LFM2.5-ColBERT and LFM2.5-Embedding, bringing fast multilingual search across 11 languages to edge devices and CPUs. The twin models tackle both single-vector speed and token-level accuracy, outperforming rivals on NanoBEIR and MKQA benchmarks. Why it matters: developers can now run high-accuracy cross-lingual search locally via GGUF without blowing up compute budgets.