

Lfms
Overview :
Liquid Foundation Models (LFMs) are a series of innovative generative AI models that achieve state-of-the-art performance across various scales while maintaining lower memory usage and higher inference efficiency. LFMs leverage computational units from dynamic systems theory, signal processing, and numerical linear algebra to handle all types of sequential data, including video, audio, text, time series, and signals. These models are general-purpose AI solutions designed to process large-scale, multimodal sequential data, enabling advanced reasoning and reliable decision-making.
Target Users :
The target audience includes businesses in finance, biotechnology, consumer electronics, and developers looking to deploy efficient AI solutions in resource-constrained environments.
Use Cases
Risk assessment and forecasting in financial services.
Drug discovery and gene sequence analysis in biotechnology.
Smart assistants and personalized recommendations in consumer electronics.
Features
LFMs in sizes 1B, 3B, and 40B achieve state-of-the-art performance in their respective categories.
LFM-1B scores the highest across various benchmarks in the 1B category, becoming the new best model at this scale.
LFM-3B ranks first among transformer, hybrid, and RNN models with 3B parameters, exceeding the previous generation's 7B and 13B models.
LFM-40B provides a new balance between model size and output quality, with its MoE architecture enabling deployment on more cost-effective hardware.
LFMs have a smaller memory footprint, especially with long inputs.
LFMs truly leverage their context length, optimizing for a 32k token context length.
LFMs are optimized for knowledge capacity, multi-step reasoning, long context recall, inference efficiency, and training efficiency.
How to Use
1. Access the Liquid Playground or Lambda interface.
2. Register and log in to gain access.
3. Choose the LFM model that suits your needs (1B, 3B, or 40B).
4. Configure model parameters according to the provided documentation and guidelines.
5. Use the Lambda API or Perplexity Labs for model inference.
6. Analyze the model output and adjust the model configuration as needed.
7. Utilize the model for specific tasks in areas such as text generation, data analysis, etc.
8. Continuously optimize model performance through community feedback and model iterations.
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