Vision-Language Models (VLMs) like LLaMA are becoming increasingly powerful at understanding and generating text grounded in visual content. They excel at tasks like image captioning, visual question answering (VQA), and multimodal reasoning—making them highly useful in a wide range of real-world applications.
But while these models perform impressively out of the box, domain-specific or task-specific use cases often demand additional tuning. This is where Supervised Fine-Tuning (SFT) comes in. By fine-tuning a pre-trained VLM on curated image–question–answer (QA) pairs, we can significantly improve its performance for specific applications.