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To build a vision-language chatbot for visual question answering, use a multimodal model combining a vision encoder with a language model. This enables multi-turn image conversations and visual instruction following across various model sizes.

Visual instruction tuning for multimodal image understanding trains a model to follow human instructions regarding image content. It combines a CLIP-based vision encoder with Vicuna or LLaMA language models to support image description and visual question answering.

Yes, you can use transformers and torch for image-based instruction following by loading a pretrained multimodal model. These frameworks provide the necessary runtime dependencies alongside pillow for image processing to enable multimodal conversations.

Multimodal image chat works across various model sizes and licenses by leveraging models that integrate a vision encoder with a language model. You can select different pretrained configurations to match your specific research or application requirements.

A limitation of using a CLIP-based vision encoder for image chat is its dependency on the pretrained representations it receives, which may constrain performance on highly specialized visual domains outside its training distribution.