LFM2.5-Encoders for Fast Long-Context Inference on CPU
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.
- ▪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.
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| Publication time | Tue, 28 Jul 2026 15:01:45 GMT |
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Back to Articles LFM2.5-Encoders for Fast Long-Context Inference on CPU Team Article Published July 28, 2026 Upvote 1 Fernando Fernandes Neto fernandofernandes Follow LiquidAI Edoardo Mosca EdoardoMosca Follow LiquidAI Maxime Labonne mlabonne Follow LiquidAI Leonie Monigatti iamleonie Follow LiquidAI Why we built a general-purpose encoder How the encoders are built Benchmark Results Inference speed on CPU and GPU LFM2.5-Encoder demos How to use and fine-tune LFM2.5-Encoders Load and run the model Fine-tuning for your task Get started with LFM2.5-Encoders Citation 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.
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