Liquid releases long-context encoders aimed at CPU workloads
Liquid AI released 230M and 350M bidirectional LFM2.5 encoders with 8,192-token context for classification, routing, extraction and scoring. Model cards, raw evaluation code, browser demos and a fine-tuning tutorial are available.
Why it matters: A task-specific encoder can be cheaper and more controllable than a generative model for high-volume classification, policy linting and routing.