Fine-Tune Florence 2: Train Vision Language Model for VQA

In this video, Mervin Praison provides a detailed tutorial on fine-tuning Florence 2, a vision language model developed by Microsoft. The tutorial covers the entire process from setting up the environment to uploading the trained model to Hugging Face. Mervin explains the importance of fine-tuning for improving the model’s accuracy in tasks such as document visual question answering (VQA) and health anomaly detection. The tutorial includes steps for configuring the GPU, installing necessary libraries, preparing the dataset, training the model, and uploading it to Hugging Face. Mervin uses the Document VQA dataset as an example, demonstrating how to train the model to answer questions based on images. He also highlights the use of Massed Compute for setting up the GPU environment and offers a discount code for the service. The video emphasizes the benefits of fine-tuning, such as enhanced model performance and the ability to customize the model for specific tasks.