Liquid AI Releases the Open-Weight LFM3 Multimodal Model Family
Liquid AI released LFM3, a family of six models in the roughly 2B-parameter range covering text, vision, audio, omni, and decision tasks on a shared hybrid backbone. The models were pretrained on 22T tokens, and the base and post-trained weights are available through Hugging Face, Liquid AI Playground, and the Liquid AI API. Liquid AI says LFM3-1.7B uses 35% fewer parameters than LFM2.5-2.6B, occupies about 1 GB at 4-bit precision, and supports a 256K-token context window.
The release provides developers with open-weight models spanning multiple modalities and specialized tasks that can be fine-tuned and deployed locally on devices including phones, laptops, and edge hardware.
Today, we’re excited to announce LFM3, our new generation of Liquid Foundation Models (LFMs) built for specialized intelligence. The LFM3 model family is multimodal by nature and brings text, vision, audio, omni, and decision models onto one new shared backbone. The first release of the LFM3 family covers models in the 2B parameter range.
Built on a new hybrid architecture and 22T tokens of pre-training, LFM3 is far more expressive than LFM2 while maintaining its efficiency. The extra capacity in a small, fast model makes LFM3 the ideal starting point in its class for specialized intelligence: this is because it can be fine-tuned locally, it can run on any device, and it is capable enough to become a reliable expert on a specialized task.
The base and post-trained models are open-weight and available today on Hugging Face, our Playground, and the Liquid AI API. Check out our docs on how to run and fine-tune them locally.
#Liquid AI#LFM3#LFM3-1.7B#LFM3-1.7B-Base#LFM3-VL-2B#LFM3-Audio-2B#LFM3-Omni-2B#d1-2B#d1-Omni-2B#Hugging Face