llava

Visual question answering is achieved by pairing a vision encoder with a large language model to produce image-grounded dialogue. This approach enables multi-turn conversations referencing visual content and scene descriptions.

Yes, visual instruction following supports configurable deployment across both CPU and GPU setups. You can select from multiple open-source model sizes, ranging from 7B to 34B parameters, to match your available compute resources.

Visual instruction following is the process of adhering to image-based instructions within interactive tasks. It unites a vision encoder with a language model to perform document understanding, describe scenes, and answer questions about visual content.

To start multimodal conversations, provide an image and ask a question. The vision-language model processes the visual input alongside text to generate image-grounded dialogue, scene descriptions, and answers about the provided visual content.

LLaVA supports multi-turn image chat by pairing a vision encoder with a large language model. This allows users to hold ongoing conversations referencing visual content, performing detailed scene description and document understanding across multiple interactions.

Limitations of open-source vision-language models include dependency on compute resources for deployment. Running larger model sizes (up to 34B) requires significant memory, though smaller 7B models offer faster but less detailed image-grounded dialogue.