👉 Liquid Foundation Models (LFMs) are large neural networks that go beyond the traditional transformer architecture, utilizing a proprietary design space that includes concepts like Liquid Time-constant Networks (LTCs), deep signal processing layers, and state-space models. This innovative approach allows LFMs to model complex sequential data with high efficiency and flexibility, surpassing many generative pre-trained Transformers (GPTs) in terms of performance and resource usage. LFMs are trained using a combination of first-principles modeling and machine learning, ensuring they capture intricate patterns in data while maintaining interpretability and scalability. This makes LFMs particularly suitable for applications requiring robust and adaptable modeling capabilities, such as video analysis, audio processing, and natural language understanding.
