

Fine-tuning ViLT
ViLT model incorporates text embeddings into a Vision Transformer (ViT), allowing it to have a minimal design for 
Vision-and-Language Pre-training (VLP). This model can be used for several downstream tasks. For the VQA task, a classifier 
head is placed on top (a linear layer on top of the final hidden state of the [CLS] token) and randomly initialized. 
Visual Question Answering is thus treated as a classification problem.
More recent models, such as BLIP, BLIP-2, and InstructBLIP, treat VQA as a generative task. Later in this guide we 
illustrate how to use them for zero-shot VQA inference. 
Before you begin, make sure you have all the necessary libraries installed. 