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LFMs

A new generation of generative AI models

#AI
#machine learning
#generative model
#multimodal data
#memory efficiency
LFMs

Product Details

Liquid Foundation Models (LFMs) are a new family of generative AI models that achieve state-of-the-art performance at all scales while maintaining a smaller memory footprint and more efficient inference efficiency. LFMs utilize computational units from dynamic systems theory, signal processing, and numerical linear algebra to process any type of sequence data including video, audio, text, time series, and signals. These models are general-purpose AI models designed to process large-scale sequential multi-modal data, enable advanced reasoning, and make reliable decisions.

Main Features

1
1B, 3B and 40B LFMs achieve state-of-the-art performance at their respective scales.
2
The LFM-1B achieved top scores across various benchmarks in the 1B category, becoming the new best model in this scale.
3
LFM-3B ranks first among converter, hybrid and RNN models with 3B parameters, also surpassing the previous generation 7B and 13B models.
4
The LFM-40B offers a new balance between model size and output quality, and its MoE architecture enables it to be deployed on more cost-effective hardware.
5
LFMs have a smaller memory footprint, especially with long inputs.
6
LFMs really make use of their context length, optimizing for the 32k token context length.
7
LFMs are optimized in terms of knowledge capacity, multi-step reasoning, long context recall, reasoning efficiency and training efficiency.

How to Use

1
1. Access the Liquid Playground or Lambda interface.
2
2. Register and log in to gain access.
3
3. Choose the LFM model (1B, 3B or 40B) that suits your needs.
4
4. Configure model parameters according to the provided documentation and guidance.
5
5. Use Lambda API or Perplexity Labs for model inference.
6
6. Analyze the model output and adjust the model configuration as needed.
7
7. Use models to perform tasks in specific fields, such as text generation, data analysis, etc.
8
8. Continuously optimize model performance through community feedback and model iteration.

Target Users

The target audience includes companies in industries such as finance, biotechnology, and consumer electronics, as well as developers looking to deploy efficient AI solutions in resource-constrained environments.

Examples

Risk assessment and prediction in financial services.

Drug discovery and gene sequence analysis in biotechnology.

Intelligent assistants and personalized recommendations in consumer electronics.

Quick Access

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Categories

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› AI model
› Model training and deployment

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