LiquidAI LFM2.5-Embedding-350M-GGUF Turns Text Into Searchable Data

LFM2.5-Embedding-350M-GGUF is a new model designed to turn text into searchable data across 11 different languages. It works as a dense bi-encoder that creates a single vector for each document to enable reliable cross-lingual search. Users can easily plug it into their existing retrieval pipelines as a direct replacement.

LiquidAI developed this tool to provide a fast and efficient way to process multilingual text. The team built it on the efficient LFM2 backbone to achieve speeds comparable to much smaller models. Their solution focuses on delivering best-in-class accuracy for a model of this size without sacrificing performance.

Speed and multilingual search capabilities

This tool is built for developers and teams who need reliable cross-lingual search without heavy computing power. Anyone running local AI systems can use it to quickly embed queries and rank documents by similarity. It provides a simple way to upgrade current document retrieval setups to handle multiple languages efficiently.

Project notes and integration details

Users can run the model locally by starting the llama-server with the embeddings flag enabled. The system requires users to apply specific prompt prefixes like query and document to embed text properly. Results are then ranked by cosine similarity to find the most relevant matches for a given search.

"You can use it as a drop-in replacement in your current RAG pipelines." Source: Reddit