LFM2.5 230M

LFM2.5 VL 3B

Liquid AI

Liquid’s larger LFM2.5 vision model integrates a 400M SigLIP2 NaFlex encoder with the 2.6B language backbone. It strengthens screen understanding, grounding, multi-image processing, and function calling. The post-trained model answers directly without long reasoning traces, making it suitable for interactive visual assistants. Downloadable weights use the Liquid Open Model License, whose commercial-use conditions should be checked before deployment. Local inference supports private customization, while task reliability and performance require evaluation on the intended hardware.

LFM2.5 2.6B

Liquid AI

This dense agent model scales pretraining to about 34T tokens and adds a dedicated 128K context-extension phase. Post-training combines teacher specialization, on-policy distillation, and reinforcement learning for planning and chained tool calls. The model is designed for smartphones, CPUs, and GPU serving, with downloadable base and post-trained checkpoints. Downloadable weights use the Liquid Open Model License, whose commercial-use conditions should be checked before deployment. Local inference supports private customization, while task reliability and performance require evaluation on the intended hardware.

LFM2.5 8B A1B

Liquid AI

The sparse LFM2.5 tier combines expert routing, short convolutions, and attention for local agents. It extends context to 128K and expands the tokenizer for non-Latin scripts. Unlike its preceding LFM2 checkpoint, the post-trained version always generates reasoning traces. The model card’s measured parameter counts are more precise than the rounded A1B product name. Downloadable weights use the Liquid Open Model License, whose commercial-use conditions should be checked before deployment. Local inference supports private customization, while task reliability and performance require evaluation on the intended hardware.