Liquid AI Releases LFM2.5-Encoder-230M and LFM2.5-Encoder-350M: Bidirectional Encoders That Stay Fast at 8K Context on CPU
Liquid AI has released two open-weight bidirectional encoders, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M. Both are masked language models built on the LFM2 hybrid backbone. Both carry an 8,192-token context. Encoders sit underneath classifiers, intent routers, safety filters, and PII detectors. Those jobs run continuously, usually without a GPU, and increasingly on longer inputs. BERT established the class. ModernBERT pushed its accuracy, speed, and context. Liquid AI’s argument is
Liquid AI released two open-weight bidirectional encoder models that process text in both directions and can handle 8,192-token contexts while running efficiently on CPUs without GPUs. These encoders are built from decoder backbones that were modified with bidirectional attention masks and a masked language modeling training approach, making them suitable for tasks like document classification, safety filtering, and personally identifiable information detection. The models were evaluated against 14 competing models on 17 benchmark tasks, with the larger 350M version ranking fourth overall and outperforming several established competitors on classification accuracy. The release targets deployment scenarios where GPU resources are unavailable or undesirable, including edge devices, regulated industries with sensitive data, and cost-sensitive processing pipelines that need efficient initial screening before larger models.
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