LFM2.5-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.

With these, you can build intent routers, policy linters, PII detectors, and text classifiers that run cheaply, all day. See the live demos below.

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 built a general-purpose model instead of reusing the retrievers.

Encoders power many modern production NLP applications: classifiers, intent routers, safety filters. These jobs run all day, usually on CPU, on ever-longer inputs. BERT established this class of model, and recently ModernBERT pushed its accuracy, speed, and context further. LFM2.5-Encoders take the next step on the LFM2 architecture, where cost grows slowly as inputs grow.

We initialize the encoders from their respective LFM2 decoder backbones: LFM2.5-230M and LFM2.5-350M . Then we turn each causal decoder into a bidirectional encoder with a few changes:

We fine-tune each model fully on every task and report the resulting score. Across the table, that's 14 models on 17 tasks pulled from GLUE, SuperGLUE, and multilingual classification.

We report the mean across five held-out seeds, so the numbers are stable run to run. The full framework and raw results are open-sourced .

LFM2.5-Encoder-350M ranks fourth of the 14 models. The three ahead of it are all larger, including a 3.5B model nearly 10 times its size. LFM2.5-Encoder-230M beats ModernBERT-base and every EuroBERT model, while being smaller than most of them. Both also score well above our own LFM2.5-Retrievers here.

Our encoders inherit the LFM2 backbone's fast inference. Since both our encoders and ModernBERT support an 8,192-token context, we measure speed across the full range.

Our encoders show their biggest edge on CPU. Here, LFM2.5-Encoder-230M is the fastest at every sequence length (even faster than the smaller ModernBERT-base for short inputs). With increasing input length, throughput decreases sharply for ModernBERT, while our LFM2.5-Encoders rise into the mid-range before tapering. At 8,192 tokens, ModernBERT-base takes over a minute and a half per forward pass versus about 28s for LFM2.5-Encoder-230M. This is about 3.7x faster. For developers, that means you can scan or classify a full contract, transcript, or long support thread in under 30 seconds on a laptop CPU.