Qualcomm · Hugging Face
Build specialized multimodal models with LFM3
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LFM3 is their new generation of Liquid Foundation Models (LFMs), built to be the starting point for specialized intelligence.
Key facts
- Jointly trained across 15 embodiments, the VLA achieves 96.2% average success across the four LIBERO suites and 45.7% on RoboCasa-GR1 tabletop tasks, generating action chunks in under 50 ms
- Per parameter, LFM3-1.7B matches LFM2-1.2B on short prompts across edge devices, and with 8K tokens in context it is about 25% faster
- The search now covers 14 device and backend combinations, from phones and a Raspberry Pi 5 to laptops, cloud CPUs, GPUs, and the Qualcomm Hexagon NPU
- Compared with LFM2.5-VL-3B, it has fewer parameters (1.9B against 3.1B) yet raises the average across 62 vision benchmarks from 57.6 to 71.4, on par with Gemma 4 12B, a model more than six times
Summary
More capability per parameter: a new tri-hybrid architecture and training recipe allow LFM3-1.7B to outperform LFM2.5-2.6B while using 35% fewer parameters. Every modality on one backbone: six 2B-class models (text, vision, audio, omni, and decision) trained from LFM3-1.7B-Base, including their first omni model and video input for their vision model. Built for the edge: the text model fits in about 1 GB at 4-bit precision, new in-house vision and audio encoders speed up processing, and the text model’s 256K context window lets agents run on-device. Made to be specialized: every model fine-tunes on a single GPU, so you can adapt it to your data, terminology, and deployment environment.