Liquid AI Introduces New Class of Foundational Models, Advancing AI Performance and Efficiency
Liquid AI, a pioneer in artificial intelligence innovation, is set to redefine the AI landscape with the launch of its Liquid Foundational Models (LFMs). These models bring unprecedented levels of performance and efficiency to generative AI, offering powerful solutions for industries looking to harness artificial intelligence more effectively on device or on server. A technical whitepaper about model performance will be available on September 30, 2024, at 11 am EST.
Key Highlights:
- LFMs Introduce a New Scaling Law for Generative AI: Markedly improving quality, cost-effectiveness, and power efficiency compared to current GPT models.
- Innovative Architecture: Leveraging dynamical systems and advanced mathematics, LFMs achieve superior performance with fewer parameters.
- Initial Model Release: The lineup includes models with 1B, 3B, and 40B parameters, each setting new benchmarks in their respective categories.
- LFM-1B: A 1.3-billion-parameter model achieving the highest scores across benchmarks in its class.
- LFM-3B: A 3.1-billion-parameter model that outperforms many 7B and 13B models, making it ideal for edge deployment and resource-constrained environments.
- LFM-40B: A 40.3-billion-parameter Mixture of Experts (MoE) model designed for complex tasks, offering performance comparable to much larger models while remaining cost-effective.
Real-World Applications:
- Enterprise: LFMs enable the design of more powerful AI assistants and analytics tools that can run efficiently on existing infrastructure, reducing operational costs.
- On-Device: LFMs prioritize privacy, low latency, security and offline accessibility, making them suitable for mobile and edge deployments.
“We build AI systems from a new set of algorithms for data curation, pre-, mid- and post- training, model architecture design, and evaluation metrics” said Dr. Ramin Hasani, Liquid AI CEO and co-creator of the LFMs. “Our new methods in designing foundation models unlock a new scaling law for LLMs. we've improved quality, cost-effectiveness, and power efficiency compared to today’s models at every scale.”
These models have demonstrated impressive results on standard LLM evaluation benchmarks. Additionally, LFMs maintain near-constant inference time and memory complexity, allowing for longer input sequences without significant increases in computational resources.
Advancements in AI Architecture:
LFMs introduce a new design space for foundational models by employing adaptive linear operators rooted in dynamical systems and signal processing. This approach unifies and extends existing computational methods in deep learning, providing a systematic way to explore architectural possibilities. The models are highly adaptable, optimized for various hardware platforms including NVIDIA, AMD, Qualcomm, Cerebras, and Apple devices.
October 23 Launch Event at MIT’s Kresge Auditorium
Liquid AI will officially unveil the Liquid Foundational Models on October 23 at MIT's Kresge Auditorium. The event will feature live demonstrations and in-depth discussions with the founders about the models' impact on the future of AI. Select members of the media are invited to attend and can schedule advance interviews and demonstrations.