LFM2.5-Encoders
Liquid AI's open-weight LFM2.5-Encoder-230M and -350M models, bidirectional encoders tuned for fast long-context CPU inference.
Overview
Liquid AI released LFM2.5-Encoder-230M and LFM2.5-Encoder-350M on July 28, 2026, open-weight bidirectional encoder models converted from its LFM decoder architecture via attention-mask modification and symmetric convolution padding. The models support up to 8,192-token context with gradually increasing latency, match or beat larger encoders on GLUE, SuperGLUE, and multilingual benchmarks, and run roughly 3.7x faster than ModernBERT-base on CPU at maximum context.
This is a sourced directory entry. Features and capabilities are reported in the linked sources; this entry does not document independent Aymorphic testing.
Key features
- Two sizes: 230M and 350M parameters, open-weight
- Bidirectional encoders derived from decoder architecture via attention-mask and convolution-padding changes
- 8,192-token context support with gradual latency scaling
- ~3.7x faster than ModernBERT-base on CPU at max context
- Supports masked language modeling for fine-tuning
Use cases
- Text classification and routing on CPU-only infrastructure
- PII detection and policy linting over long documents
- Prompt routing and spell-checking pipelines
Pricing
Free and open weight; hosted on Hugging Face.
Pricing reflects the source record and may change. Confirm current terms with the provider.
Dates and sources
- Released
- 28 Jul 2026
- Record updated
- 23 Sept 2026
- Record last checked
- 23 Sept 2026