LFM2.5-Encoders for Fast Long-Context Inference on CPU

Back to Articles

a]:hidden">

LFM2.5-Encoders for Fast Long-Context Inference on CPU

Team Article Published July 28, 2026

Upvote 45

+39

Fernando Fernandes Neto fernandofernandes Follow

LiquidAI

Edoardo Mosca EdoardoMosca Follow

LiquidAI

Maxime Labonne mlabonne Follow

LiquidAI

Leonie Monigatti iamleonie Follow

LiquidAI

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. LFM2.5-Encoders come from the same family but serve a broader purpose. They're pre-trained with a masked-language objective, so you can fine-tune them for classification, token-level tasks, and search alike. Search is just one thing an encoder enables. That's why we bu

…