Hugging Face· PlatformLFM2.5-Encoders for Fast Long-Context Inference on CPU
Today, we release two new encoder models on Hugging Face: LFM2.5-Encoder-230M and LFM2.5-Encoder-350M . They match the quality of larger models but stay fast as inputs get longer. This means you can run document-scale jobs on the hardware you already have, even on CPU. Here's what you get: Strong for their size: match or beat larger encoders on GLUE, SuperGLUE, and multilingual tasks. 8,192-token context with latency that grows slowly as inputs get longer. Fast on CPU : about 3.7× faster than ModernBERT-base at long context. With these, you can build intent routers, policy linters, PII detectors, and text classifiers that run cheaply, all day. See the live demos below. Why we built a general-purpose encoder Last month we released LFM2.5-Retrievers , built for multilingual search. …
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- Announced
- 28 Jul 2026 15:01 UTC
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