LFM2.5 VL 3B

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 230M

Liquid AI

Liquid’s smallest LFM2.5 text tier is designed for inexpensive local inference and downstream fine-tuning. Its training includes 19T tokens and a 32K context extension, followed by distillation, preference optimization, and reinforcement learning. It can select tools from natural-language instructions, but demonstrations of robot control rely on external low-level skills. 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.