Build specialized multimodal models with LFM3
Build specialized multimodal models with LFM3
Liquid AI introduces LFM3, a generation of Liquid Foundation Models in the 2B parameter range that places text, vision, audio, omni, and decision models on one shared backbone built from LFM3-1.7B-Base, which was pre-trained on 22T tokens and uses a tri-hybrid layer mix with a 256K context window. The release includes six 2B-class models, new in-house vision and audio encoders, and LiquidCodec, with every model fine-tunable on a single GPU and the text model fitting in about 1 GB at 4-bit precision. All LFM3 models are open-weight and available on Hugging Face, with fine-tuning notebooks and agent harness guides provided.
The post is a detailed technical release covering architecture changes, benchmark comparisons, and runnable code for six models, useful for developers evaluating small multimodal models for on-device deployment.
Source: Hugging Face Blog · huggingface.co