Liquid AI · Small multimodal LFMs

LFM3

LFM3 is a new generation of Liquid Foundation Models from Liquid AI, with text, vision, audio, omni and decision models on one shared backbone. The first release has six models of around 2B parameters, all branched from LFM3-1.7B-Base, which was pre-trained on 22T tokens. The architecture was found by Liquid's hardware-in-the-loop search across 14 device and backend combinations. It mixes 13 gated short-convolution layers, 5 sliding-window attention layers (256-token window) and 6 global attention layers, with head-wise gated attention, partial RoPE and sandwich norm. Liquid says LFM3-1.7B outperforms LFM2.5-2.6B with 35% fewer parameters, is about 25% faster than LFM2-1.2B at 8K tokens of context, and the page model fits in about 1 GB at 4-bit precision with a 256K context window. In Liquid's own table, LFM3-1.7B scores 71.39 on BFCL V4 and 43.40 on SWE-bench Verified (OpenCode), against 55.89 and 18.00 for LFM2.5-2.6B. Base and post-trained models are open-weight on Hugging Face, the Liquid Playground and the Liquid API, and each can be fine-tuned on a single GPU.