LFM3: A New Era for Specialized Multimodal Models
Hugging Face's LiquidAI has released LFM3, a new generation of Liquid Foundation Models (LFMs) designed to be the starting point for specialized intelligence. LFM3 brings together text, vision, audio, omni, and decision models onto one shared backbone, all in the 2B parameter range. This allows users to pick the model for their modality, fine-tune it on their own data on a single GPU, and deploy it where their application runs. LFM3-1.7B outperforms its predecessor with 35% fewer parameters and includes new features such as a tri-hybrid architecture, head-wise gated attention, partial RoPE, and sandwich norm. The vision and audio models also get two new in-house encoders, which reduce compute requirements and improve accuracy. LFM3 is built for the edge, with the text model fitting in about 1 GB at 4-bit precision and a 256K context window that lets agents run on-device. Each model can be fine-tuned on a single GPU, making it easier to adapt to specific data, terminology, and deployment environments.
Hugging Face's LiquidAI has released LFM3, a new generation of Liquid Foundation Models (LFMs) designed to be the starting point for specialized intelligence. LFM3 brings together text, vision, audio, omni, and decision models onto one shared backbone, all in the 2B parameter range.
LFM3-1.7B outperforms its predecessor with 35% fewer parameters and includes new features such as a tri-hybrid architecture, head-wise gated attention, partial RoPE, and sandwich norm.
The vision and audio models also get two new in-house encoders, which reduce compute requirements and improve accuracy.
LFM3 is built for the edge, with the text model fitting in about 1 GB at 4-bit precision and a 256K context window that lets agents run on-device.
Each model can be fine-tuned on a single GPU, making it easier to adapt to specific data, terminology, and deployment environments.
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Source: Hugging Face · Published 2026-10-27. Reported by Benny Byte.