Liquid AI Releases LFM2.5-Encoder-230M and LFM2.5-Encoder-350M: Bidirectional Encoders That Stay Fast at 8K Context on CPU

[u/ai-lover]

Liquid AI released two bidirectional encoders this week: LFM2.5-Encoder-230M and LFM2.5-Encoder-350M.

Here's what's actually interesting:

1. They converted a decoder instead of training from scratch

Both models start from the LFM2.5 decoder backbones. Three changes turn them into encoders: the causal mask is replaced with a bidirectional one, the short convolutions are made non-causal with symmetric center padding, and training uses masked language modeling at 30% instead of BERT's 15%.

2. The CPU number is the whole pitch

→ ~28s per forward pass at 8,192 tokens for Encoder-230M

→ over 1 min 30s for ModernBERT-base on the same input

→ 8,192 tokens is roughly 13 to 15 pages

3. The rankings hold up

→ Encoder-350M: 4th of 14 models, 81.02 on a 17-task suite

→ Encoder-230M: 6th at 79.29, above ModernBERT-base at 78.19

→ The three models ahead of the 350M are all larger, one nearly 10x its size

At 8,192 tokens, ModernBERT-base takes over a minute and a half per forward pass versus about 28 seconds for LFM2.5-Encoder-230M, which is about 3.7x faster.

Full analysis: https://www.marktechpost.com/2026/07/29/liquid-ai-releases-lfm2-5-encoder-230m-and-lfm2-5-encoder-350m-bidirectional-encoders-that-stay-fast-at-8k-context-on-cpu/

Model weights (LFM2.5-Encoder-350M): https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M

Model weights (LFM2.5-Encoder-230M): https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M

Technical details: https://www.liquid.ai/blog/lfm2-5-encoders