Multiple model modalities
The first LFM3 release includes six 2B-class models with a shared backbone across text, vision, audio, omni and decision models.
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Productivity / product dossier
Six open-weight, 2B-class models for text, vision, audio, omni and decision tasks on edge devices.
Product brief
LFM3 is Liquid AI’s third generation of Liquid Foundation Models, with open weights and a shared backbone across text, vision, audio, omni and decision models. The first release is six 2B-class models, including a 1.7B text model that fits in about 1 GB at 4-bit and has a 256K context. Liquid says they run on phones, laptops and edge devices and can be fine-tuned on one GPU. Weights are on Hugging Face.
Why we selected it
A technically substantive release with open weights already on Hugging Face. Six small models spanning multiple modalities offer practical experimentation paths for edge deployment. Its local-compute focus is especially.
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The first LFM3 release includes six 2B-class models with a shared backbone across text, vision, audio, omni and decision models.
The 1.7B text model fits in about 1 GB at 4-bit and supports a 256K context.
Liquid AI says the models run on phones, laptops and edge devices.
Weights are available on Hugging Face, and Liquid AI says the models can be fine-tuned on one GPU.
Best-fit use cases
FAQ
LFM3 is Liquid AI’s third generation of Liquid Foundation Models. Its first release consists of six 2B-class models spanning text, vision, audio, omni and decision models.
LFM3 has open weights available on Hugging Face.
The text model has 1.7 billion parameters, fits in about 1 GB at 4-bit and supports a 256K context.
Liquid AI says the models run on phones, laptops and edge devices and can be fine-tuned on one GPU.