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← Back to Liquid Nanos LFM2.5-Encoder-230M is a multilingual bidirectional encoder built on the LFM2 architecture. At 230M parameters, it is designed to be fine-tuned into task-specific models (classification, token classification, retrieval, reranking, and semantic similarity) across 15 languages.
Use LFM2.5-Encoder-230M when you need the smallest, fastest general-purpose encoder to fine-tune. For maximum downstream quality, use LFM2.5-Encoder-350M.

Specifications

Text Classification

Sentiment, topic, intent/routing, moderation, and business-text linting.

Token Classification

Named-entity recognition, span extraction, and sequence labeling.

Fine-Tuning Backbone

A base for dense embedding or late-interaction retrievers, NLI, and semantic similarity.

Quick Start

This is a base encoder that produces general-purpose representations. For task outputs, fine-tune it with a task head for your use case. See the fine-tuning overview to get started.
Install:
Run masked-token prediction:
Load the encoder body for downstream tasks:For classification, token classification, regression, or retrieval, load the encoder body and attach your own head: