llava

Visual question answering identifies objects and scenes in an image by processing conversational queries. You send an image file and a question to the model, which analyzes the visual content and returns a text-based answer about the specific relationships or items visible.

Multi-turn image chat allows you to ask follow-up questions about the same picture while preserving conversational context. The model maintains the dialogue history, enabling continuous visual instruction following without needing to re-upload or re-explain the image content.

You need a CLIP vision encoder paired with a LLaMA or Vicuna-family language model to extract information from document images. This multimodal setup supports image preprocessing, prompt construction with an image token, and inference via a multimodal generate call.

To follow instructions for describing scenes in photos, use visual instruction following capabilities. This mechanism processes detailed prompts alongside the image to generate captions, extract information, or provide comprehensive descriptions of the visual content and object relationships.

Visual question answering works with image chat to extract text from document images by leveraging a vision-language model. It supports document-like image understanding through instruction following, allowing you to query specific text or data points within the image conversationally.

A multimodal generate call requires image preprocessing and prompt construction with an image token to properly align visual features from the CLIP encoder with the language model. This ensures accurate visual understanding and instruction following during inference.