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.
- Transformers
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: