Liquid AI ships small bidirectional encoders that hold up at 8K on CPU

Today we release LFM2.5-Encoder-230M and LFM2.5-Encoder-350M: bidirectional encoders that stay fast at long context, even on CPU. > LFM2.5-Encoder-230M: about 3.7x faster than ModernBERT-base on CPU at 8,192 tokens. Under 30s per forward pass, versus over a minute and a half. > LFM2.5-Encoder-350M: 4th of 14 models on GLUE, SuperGLUE, and multilingual classification, behind only three larger models, one of them nearly 10x its size. 🧵

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Two sizes in the same family: > LFM2.5-Encoder-350M: the higher-accuracy pick. > LFM2.5-Encoder-230M: smaller and faster, for tighter hardware or higher throughput. Both are pre-trained with a masked-language objective, so you fine-tune them for classification, token-level tasks, or retrieval. (2/6)

Built on the LFM2 hybrid backbone, adapted from causal decoders into bidirectional encoders: bidirectional attention, non-causal short convolutions, and a 30% mask rate. The backbone's cost grows slowly as inputs get longer. > Context: 8,192 tokens, about 13 to 15 pages in one forward pass. > Runs on CPU with no GPU, where the speed edge is largest. > On GPU, the same pattern holds at the longest inputs. (3/6)

See it in action. We built demos that run entirely on CPU: > Prompt routing: https://t.co/Y7CfhQrSTy > Policy linting: https://t.co/DPu9iV8mbh > Spell checker: https://t.co/92NrnWDsEo Bonus: we even run the encoder as a masked-diffusion chatbot. https://t.co/4EIW6sYZiF (4/6)