Liquid AI open-sources fast, CPU-friendly text encoders

Liquid AI released two open-weight bidirectional encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, both with 8,192-token context built on the LFM2 hybrid backbone. The 350M model ranks fourth of 14 models on a 17-task GLUE/SuperGLUE/multilingual benchmark suite, and the 230M model completes an 8K-token forward pass on CPU in about 28 seconds.

Why it matters: Small, CPU-efficient encoders matter for edge and on-device use cases — retrieval, classification, and search often just need a good encoder rather than a full generative model. Competitive benchmark results at this size make it a practical, low-cost option for deployments that can't justify running an LLM.