llava

Build a multimodal vision-language chatbot by combining a CLIP vision encoder with a Vicuna/LLaMA language model to enable multi-turn image chats and visual reasoning. This approach supports visual instruction tuning for conversational image understanding.

Visual instruction tuning for multimodal reasoning trains models to follow conversational prompts about images. It connects a CLIP vision encoder with a Vicuna/LLaMA language model to process visual inputs and generate text responses.

Visual question answering requires GPU-accelerated inference in standard ML stacks to process the combined CLIP vision encoder and Vicuna/LLaMA language model efficiently. This hardware setup handles the computational load of multimodal reasoning.

Start a multi-turn image chat by loading a pretrained vision-language model that integrates a CLIP vision encoder with a Vicuna/LLaMA backend. This enables conversational image understanding and visual question answering.

Use this multimodal approach for image-based document understanding and captioning by leveraging the combined CLIP vision encoder and Vicuna/LLaMA language model. It extracts text and reasons about visual document content.