Multimodal Large Language Models: Architectures, Training, and Real-World Applications

Author(s): Hamza Boulahia Originally published on Towards AI. A breakdown of main architectures, training pipeline stages, and where current models actually work With the rise of AI Agents in these last few years, we reached what we could describe as an inflection point, where models can no longer afford to be confined to a single modality. Image created by the AuthorThis article explores the emergence of Multimodal Large Language Models (MLLMs), which integrate multiple forms of data such as text, images, and audio for more effective AI interaction. It discusses their architectures, training methodologies, and real-world applications, emphasizing the need for models that reflect human-like understanding. The challenges of combining different modalities and the distinctions between various architecture approaches are explored, alongside practical use cases including document interpretation, visual question answering, and agent functionality within user interfaces. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI