LiquidAI has released two new multilingual retrieval models: LFM2.5-Embedding-350M, a dense bi-encoder for fast indexing, and LFM2.5-ColBERT-350M, a late-interaction model for higher accuracy. Both models have 350 million parameters, support 11 languages, and are designed to be drop-in replacements for existing RAG pipelines, offering efficient and reliable cross-lingual search capabilities. AI
IMPACT These models offer efficient and accurate multilingual retrieval, potentially improving RAG pipelines for cross-lingual search applications.
RANK_REASON New model release from a notable AI lab. [lever_c_demoted from frontier_release: ic=2 ai=1.0]
- LFM2.5-350M-Base
- LFM2.5-ColBERT-350M
- LFM2.5-Embedding-350M
- Lfm2BidirectionalModel
- LiquidAI/LFM2.5-Embedding-350M
- sentence_transformers
- LiquidAI
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