LFM
A series of foundation models developed by Liquid AI, based on liquid neural network and state-space model principles rather than the mainstream Transformer architecture. The series is optimized for inference efficiency and memory usage, making it suitable for deployment in resource-constrained environments such as edge devices.
Core Facts
Timeline (last 90 days)
LFM is Liquid AI's foundation model product line, positioned as a lightweight, efficient, locally deployable general-purpose model family; LFM3 is the third-generation version of the series, with significantly expanded multimodal capabilities compared with its predecessors.
RIPL currently only serves the LFM series, and its range of model choices is far smaller than the open model library supported by vLLM.
RIPL has tailored batch size, operator fusion, and memory layout to the LFM structure, which is where specialized engines easily achieve multiplicative advantages.
The LFM series consists of small-parameter models designed for edge and on-device use, and the 1.2B size means it can only be optimized through extreme compression of the inference path.
Liquid AI has announced it will release a new generation of LFMs (Liquid Foundation Models).
Current information remains quite limited; specific parameter scales, benchmark scores, license details, and comparisons with similar open-source models all await official confirmation.
The teaser posed the question "what size do you want," implying that this release may include multiple model versions at different parameter scales.
Liquid Foundation Models (LFM) is a class of foundation models launched by Liquid AI whose technical foundation differs from the mainstream Transformer architecture.
LFM draws in its architecture from aspects of State Space Models (SSM) and liquid networks, aiming to reduce computational complexity when processing long sequences.
All Facts (7)
Liquid AI's technical foundation is Liquid Neural Networks, inspired by research on the nervous system of the nematode C. elegans.
65%UnverifiedLFM is Liquid AI's foundation model product line, positioned as a lightweight, efficient, locally deployable general-purpose model family; LFM3 is the third-generation version of the series, with significantly expanded multimodal capabilities compared with its predecessors.
50%UnverifiedRIPL currently only serves the LFM series, and its range of model choices is far smaller than the open model library supported by vLLM.
50%UnverifiedThe LFM series consists of small-parameter models designed for edge and on-device use, and the 1.2B size means it can only be optimized through extreme compression of the inference path.
50%UnverifiedLiquid Foundation Models (LFM) is a class of foundation models launched by Liquid AI whose technical foundation differs from the mainstream Transformer architecture.
50%UnverifiedLFM draws in its architecture from aspects of State Space Models (SSM) and liquid networks, aiming to reduce computational complexity when processing long sequences.
50%UnverifiedRIPL has tailored batch size, operator fusion, and memory layout to the LFM structure, which is where specialized engines easily achieve multiplicative advantages.
50%