Liquid Foundation Models (LFMs): Next-Generation Generative AI Models by Liquid AI
Liquid Foundation Models (LFMs) are a series of next-generation generative AI models developed by Liquid AI. These models use an innovative non-Transformer architecture, designed to offer efficient memory usage and inference performance.
Main Versions
LFM-1B
- Number of Parameters: 130 million
- Features: LFM-1B sets a new benchmark for models of its size, significantly outperforming Transformer models of similar scale, such as Meta's Llama 3.2-1.2B and Microsoft's Phi-1.5.
- Application Scenarios: Suitable for resource-constrained environments, capable of running efficiently on smaller hardware.
LFM-3B
- Number of Parameters: 310 million
- Features: LFM-3B not only outperforms other 3B models but also exceeds some models with 7B and 13B parameters. It performs well in multiple benchmark tests while requiring only 16 GB of memory, compared to Meta's Llama-3.2-3B, which needs over 48 GB.
- Application Scenarios: Particularly suitable for mobile applications and edge deployments, capable of running efficiently on memory-limited devices.
LFM-40B
- Number of Parameters: 4 billion
- Features: LFM-40B adopts a "Mixture-of-Experts" (MoE) architecture, activating 1.2 billion parameters during runtime. Its performance is comparable to larger models, while achieving higher throughput and cost efficiency.
- Application Scenarios: Suitable for complex tasks requiring high performance and high throughput, deployable on more cost-effective hardware.
Application Scenarios
- Autonomous Driving and Robotic Control
LFMs perform well in autonomous driving and robotic control, capable of handling complex navigation and control tasks. Their adaptability, due to liquid neural networks, makes them particularly effective in dynamic environments.
- Data Analysis
LFMs can efficiently process and analyze various types of continuous data, including video, audio, and time series data. This makes them valuable in fields such as financial market analysis and weather forecasting.
- Biomedical
LFMs also have extensive applications in the biomedical field, particularly in analyzing biological data such as DNA and RNA. They can even assist in designing new CRISPR gene editing systems.
- Text Processing
LFMs excel in text processing tasks, including document analysis, summarization, and context-aware chatbots. Their efficient inference capabilities provide significant advantages in these applications.
- Edge Computing
Due to their efficient memory usage and inference performance, LFMs are well-suited for deployment on edge devices such as mobile applications, drones, and IoT devices. These models can operate efficiently in resource-constrained environments.
- Financial Services
In the financial services sector, LFMs can be used for risk assessment, market forecasting, and customer behavior analysis. Their efficient data processing capabilities enable rapid analysis of large financial datasets, providing accurate predictions and decision support.
- Consumer Electronics
LFMs also have applications in consumer electronics, such as smart home devices and personal assistants. Their efficient inference capabilities and low memory footprint allow them to enable smart features on various consumer electronics devices.
- Generative Models
LFMs can be used for generative tasks, such as image generation, music creation, and content generation. Their powerful generative capabilities make them valuable in the creative industry.
Liquid Foundation Models (LFMs) are currently closed-source, meaning their code and detailed implementations are not publicly available.
