llava

Vision-language chat works by merging a vision encoder with a large language model to enable image-based conversation. This allows the system to process visual content and generate text responses for multi-turn image reasoning and visual question answering tasks.

Yes, you need compatible weights to run multimodal inference. The skill requires a CLIP-ViT vision encoder and a Vicuna or LLaMA language model, along with dependencies like transformers and torch, to successfully execute image-based conversation.

Yes, you can use this for multi-turn visual question answering on documents. The merged vision encoder and language model support image-based instruction following and document understanding, allowing interactive clarification of details within the image.

The best way to experiment with different vision encoders and LLMs is using this skill's out-of-the-box multimodal capability. It provides an easy setup to combine various encoders with language models for interactive image understanding without heavy manual configuration.

Running image-based instruction following requires compatible weights and specific dependencies including transformers and torch. Without these prerequisite components properly configured, the vision encoder and language model integration cannot execute inference for visual reasoning tasks.