Liquid AI Releases LFM2.5-Encoder-230M and LFM2.5-Encoder-350M: Bidirectional Encoders That Stay Fast at 8K Context on CPU
MarkTechPost Asif Razzaq ● Covered by 2 sources
Liquid AI released two open-weight bidirectional encoders, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, converted from their LFM2.5 decoder backbones to handle masked language modeling with 8,192-token context. The 350M model scores 81.02 on a 17-task benchmark, ranking fourth behind only larger models. These encoders enable cost-effective text classification and content filtering on CPUs without GPUs, suitable for edge devices and regulated environments handling long documents.
Why it matters
Liquid AI released two open-weight bidirectional encoders, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M. Both carry an 8,192-token context and are built on the LFM2 hybrid backbone. The 350M ranks fourth of 14 models on a 17-task GLUE, SuperGLUE, and multilingual suite, behind only larger models. The 230M clears one 8K-token forward pass on CPU in about 28 seconds. The post Liquid AI Releases LFM2.5-Encoder-230M and LFM2.5-Encoder-350M: Bidirectional Encoders That Stay Fast at 8K Context on CPU appeared first on MarkTechPost.
