Multimodal LLMs in mAIstro

What is it?

Multimodal capabilities in large language models (LLMs) refer to their ability to process and generate content across multiple modalities, such as text, images, and even audio. This allows LLMs to understand and interact with the world in a more holistic and natural way, going beyond the traditional text-based interactions.

Why is it important?

Multimodal capabilities are crucial for a wide range of applications, particularly in areas like visual question answering, image captioning, and image-to-text generation. These capabilities enable LLMs to understand and reason about the world in a more comprehensive manner, allowing for more intuitive and user-friendly interactions.

How does it work?

Multimodal LLMs typically leverage techniques like transfer learning, where the model is first trained on a large corpus of text data, and then fine-tuned on datasets that combine text and images. This allows the model to learn the relationships between visual and textual information, enabling it to generate relevant and coherent responses to queries that involve both modalities.