Liquid AI releases LFM3 open-weight model family with 256K context

Liquid AI has released the Liquid Foundation Model 3 (LFM3) family under open weights, comprising six 2-billion-parameter-class models spanning text, vision, audio, omni-modal processing, and two specialised decision models (d1-2B and d1-Omni-2B). Built on LFM3-1.7B-Base and pre-trained on 22 trillion tokens of curated multimodal data, the architecture replaces quadratic self-attention with dynamic continuous-time state representations.

The base model outperforms the prior LFM2.5-2.6B release across standard reasoning benchmarks while reducing parameter count by 35%. LFM3-1.7B scores competitively against larger open models on instruction following and mathematical problem-solving while operating with substantially lower compute overhead. The post-trained decision variants incorporate reinforcement learning loops tailored for multi-step agent actions and structured tool use.

The models support a 256k-token context window, require approximately 1 GB of memory in 4-bit quantisation, and can run inference and parameter-efficient fine-tuning on a single consumer GPU or high-end mobile chipset. Weight checkpoints, model cards and inference recipes are published on Hugging Face under the Liquid Community Licence.