llava

Visual question answering for multi-turn image conversations is enabled by combining a CLIP vision encoder with a Vicuna or LLaMA language backbone, allowing users to ask natural-language questions and receive coherent, context-aware answers about image content.

Yes, vision-language chat inference supports GPU execution with FP16 or 4-bit quantization modes to reduce memory requirements, alongside optional Gradio serving for interactive deployment and multi-turn context handling.

Generating detailed image captions from document images is best handled through instruction following capabilities that produce scene summaries and document-image understanding, maintaining context across multi-turn conversations for follow-up queries.

Multi-turn image chat maintains context across questions about the same image, enabling follow-up queries and clarifications without piecing together separate vision and language tools for each interaction.

Serving an interactive vision-language chatbot with Gradio provides a web interface for conversational image understanding, supporting real-time visual question answering and multi-turn image conversations using the CLIP vision encoder and LLM backend.