Liquid Ai has introduced LFM2.5, a series of compact on-device foundation models designed to enhance the performance of agentic applications by offering higher quality, reduced latency, and broader modality support within the ~1 billion parameter range. Building on the LFM2 architecture, LFM2.5 scales pretraining from 10 trillion to 28 trillion tokens and incorporates expanded reinforcement learning post-training to improve instruction-following capabilities. This release includes five open-weight model instances derived from a single architecture, including a general-purpose instruct model, a Japanese-optimized chat model, a vision-language model, a native audio-language model for speech input and output, and base checkpoints for extensive customization. This matters as it enables more efficient and versatile on-device AI applications, broadening the scope and accessibility of AI technology.
Liquid AI has introduced LFM2.5, a new family of compact on-device foundation models designed to enhance the performance of agentic applications. These models offer improved quality, reduced latency, and support for a wider range of modalities, all within the ~1 billion parameter class. LFM2.5 builds upon the LFM2 architecture with pretraining scaled from 10 trillion to 28 trillion tokens and expanded reinforcement learning post-training, enabling better instruction following. This advancement is crucial as it allows for more efficient and versatile AI applications directly on devices, enhancing user experience and functionality.