Florence-VL is a visual language model that enhances the processing capabilities of visual and language information by introducing generative visual encoders and deep breadth fusion technology. The significance of this technology lies in its ability to improve machines' understanding of images and text, achieving better performance in multimodal tasks. Florence-VL is developed based on the LLaVA project, providing code for pre-training and fine-tuning, model checkpoints, and demonstrations.