Hugging Face、CPU での高速長文脈推論向けエンコーダー「LFM2.5」を公開

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• 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. 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 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.

How the encoders are built

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:

• Bidirectional attention mask: each token now sees the tokens on both sides, not just the ones before it.

• Non-causal short convolutions: we pad them symmetrically so each token's convolution mixes in its neighbors on both sides.

• Masked language modeling: we mask 30% of the tokens during training.

We train both models in two stages:

• General language competence: a short-context masked-language objective on a large web corpus at a 1,024-token context.

• Long-context adaptation: extending context to 8,192 tokens on the full data mix, strengthening factual, legal, and multilingual competence.

Benchmark Results

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.

Inference speed on CPU and GPU

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.

On GPU, a similar pattern holds with a smaller margin: ModernBERT-base leads below ~1K tokens on the Apple GPU. Our encoders take the lead from about 2K tokens. This shows that for long inputs, LFM2.5-Encoders are the faster choice, and if you're running on CPU, dramatically so.

LFM2.5-Encoder demos

We built the demos below from fine-tuned LFM2.5-Encoders. Each one runs in a CPU-only Hugging Face space:

• Zero-shot prompt routing: define your own routing lanes as free text. The model scores the whole prompt against every lane in one pass.

• Zero-shot policy linting: check text against your company's rules, written as free text. It scores every token against every rule in one pass.

• Spell checking: correct misspellings token by token.

• PII detection: spot and remove 40 kinds of personal information across 16 languages.

• Masked-diffusion text generation (bonus): run the encoder as a chatbot that generates text by iteratively unmasking instead of left to right.

How to use and fine-tune LFM2.5-Encoders

Reach for an LFM2.5-Encoder when you have a high-volume understanding task, such as classification, routing, extraction, or scoring, that runs constantly and has to stay cheap and fast. For jobs like these, a fine-tuned encoder is smaller, faster, and far cheaper to run than a generative LLM, and it fits on the CPUs you already have.

Between the two encoder sizes:

• LFM2.5-Encoder-350M: choose it when accuracy matters most.

• LFM2.5-Encoder-230M: choose it for tighter hardware or higher throughput.

You can start in a few lines. Load a model with transformers. Then run it directly for masked-token prediction, or attach your own head and fine-tune it for your task.

Load and run the model

Install the latest version of transformers:

pip install -U transformers

Run masked-token prediction:

from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch

model_id = "LiquidAI/LFM2.5-Encoder-230M"  # or "LiquidAI/LFM2.5-Encoder-350M"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
mlm = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True)

text = f"The capital of France is {tok.mask_token}."
enc = tok(text, return_tensors="pt")
with torch.no_grad():
    logits = mlm(**enc).logits
pos = (enc["input_ids"][0] == tok.mask_token_id).nonzero()[0].item()
print([tok.decode([t]).strip() for t in logits[0, pos].topk(5).indices.tolist()])
# -> ['Paris', 'Strasbourg', 'Paris', 'Lyon', 'Versailles']

For downstream tasks, load the encoder body and attach your own head (classification, token classification, regression, retrieval):

from transformers import AutoModel

body = AutoModel.from_pretrained(model_id, trust_remote_code=True)

If your GPU supports it, use Flash Attention 2 for the highest efficiency:

pip install flash-attn

Fine-tuning for your task

A base encoder gives you general-purpose representations, not task outputs. So you fine-tune it for each task. Our fine-tuning tutorial walks through fine-tuning on long legal documents with an 8k context.

Get started with LFM2.5-Encoders

Both encoders are open-weight and available on Hugging Face today:

• Download: LFM2.5-Encoder-230M and LFM2.5-Encoder-350M on Hugging Face.

• Try: run the demos above in your browser, no setup needed.

• Fine-tune: adapt an encoder to your task with our fine-tuning tutorial.

We can't wait to see what you build.

Citation

If you use this work, please cite the release blog:

@article{liquidAI2026Encoders,
  author = {Liquid AI},
  title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2-5-encoders},
}