Building Multimodal Chatbots with Vision Language Model Fine-tuning

Modern AI is no longer limited to text; understanding how to integrate visual data is the next step in building truly intelligent applications. This course provides a clear path through the foundations of Vision Language Models (VLMs), teaching you how to fine-tune these models and deploy them using scalable cloud environments like RunPod. You will start by mastering the core terminology and concepts behind vision-text alignment before moving into practical implementation. By the end of this course, you will understand how to bridge the gap between computer vision and natural language processing to create more interactive AI systems. What you'll learn: - Understand the core architecture of Vision Transformers and multimodal processing - Configure cloud-based GPU environments for efficient model training and fine-tuning - Apply fine-tuning techniques to adapt pre-trained models for specific visual tasks - Implement Model Context Protocol (MCP) to enhance chatbot capabilities and tool integration - Practice building a text-and-image response system through structured written exercises - Learn modern prompt engineering strategies specifically tailored for multimodal interactions The course begins with foundational definitions and the mechanics of how models process visual tokens alongside text, followed by step-by-step written guides on fine-tuning workflows and deployment strategies. This course is designed for beginners interested in AI development, requiring no prior experience with multimodal models or fine-tuning. Start building your own multimodal AI applications today.