llava

To build a chatbot for visual question answering, you need a multimodal vision-language architecture that connects a CLIP vision encoder with a Vicuna or LLaMA-style language model to process images and generate grounded text responses.

Image captioning and document understanding use multimodal models to turn photos, screenshots, and document images into detailed natural language explanations by analyzing the visual content directly.

Fine-tuning a vision-language model for product prototypes requires applying quantization options and training paths to adapt the multimodal architecture to your specific visual question answering or image chat dataset.

Multi-turn multimodal chat works by maintaining visual context from an initial image while answering sequential natural language questions, allowing users to inspect a scene and count objects across a conversation.

Implementing visual question answering requires a CLIP vision encoder, a Vicuna or LLaMA-style language model, and image preprocessing to translate visual content into grounded natural language answers.

Summarizing dashboard screenshots is best handled by multimodal vision-language models that analyze the image and generate explanations of charts, labels, and anomalies without needing a separate vision pipeline.