kyegomez/MultiModalMamba

A novel implementation of fusing ViT with Mamba into a fast, agile, and high performance Multi-Modal Model. Powered by Zeta, the simplest AI framework ever.

What it solves

Multi Modal Mamba (MMM) addresses the need for AI models that can process and interpret multiple data types—specifically text and images—concurrently, rather than treating them as separate one-dimensional streams of information.

How it works

MMM integrates a Vision Transformer (ViT) with the Mamba architecture to create a high-performance multi-modal model. It is built on the Zeta AI framework and allows for the fusion of different data modalities using customizable methods (such as MLP fusion). The model can process text, images, audio, and video tensors simultaneously.

Who it’s for

It is designed for developers and enterprises looking to build and deploy multi-modal AI systems that require high performance, speed, and the ability to customize the model architecture for specific tasks.

Highlights

  • Multi-modal capabilities: Processes text and image data simultaneously, with support for audio and video tensors.
  • Customizable architecture: Offers deep configuration of parameters like depth, dropout, heads, and fusion methods.
  • Embedding access: Includes a return_embeddings option to retrieve intermediate representations for feature extraction or transfer learning.
  • Zeta framework integration: Built on a minimalist framework designed to streamline model management.

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