Introduction

In this section, we will provide an overview of the core components involved in our multimodal AI model architecture. The components include text, vision, audio, and animation models, as well as the necessary infrastructure for integrating these modalities into a cohesive system. Understanding these components and their interactions is crucial for appreciating the model's capabilities and the complexity of its training process.

Core Components

Text Model

The text model is the backbone of the language understanding capabilities of our system. It processes and generates text, enabling the AI to understand and respond to natural language inputs. This component is typically based on large language models such as the Meta-Llama or Vicuna series, which are fine-tuned for specific tasks.

• Example Model: meta-llama/Meta-Llama-3-8B-Instruct
• Role: Handles natural language processing and generation.

Vision Model

The vision model is responsible for processing visual data, such as images and videos. It extracts meaningful features from visual inputs and integrates this information with other modalities to enhance the AI's understanding of the context.

• Example Model: openai/clip-vit-large-patch14
• Role: Processes and interprets visual data.

Audio Model

The audio model handles audio inputs, enabling the AI to process and understand spoken language, sounds, and other auditory information. This component is crucial for applications involving speech recognition and audio analysis.

• Example Model: (Model ID specified by the user)
• Role: Processes and interprets audio data.

Animation Model

The animation model is used to generate and control animations, such as facial expressions and body movements, making the AI more interactive and expressive. This component is particularly useful for creating realistic avatars and virtual assistants.

• Example Model: (Model ID specified by the user)
• Role: Generates and controls animations.

Model Stack