Integrate a vision encoder with a language model for image-grounded conversation.
npx skills add https://github.com/arsity/scholar-tools --skill llava-arsity
Please help me install this Agent Skill. Skill: llava Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/18-multimodal/llava Command: npx skills add https://github.com/arsity/scholar-tools --skill llava-arsity
Enables multimodal image-grounded conversation by integrating a vision encoder with a language model to understand and discuss visual content.
Load a LLaVA model and start a multimodal chat by feeding an image and a question.
High-intent search queries and answers about installing and using this skill.
Vision-language chat for visual question answering is enabled by integrating a vision encoder with a language model to process image inputs and generate text responses across multi-turn dialogues.
Image description and scene understanding are achieved by feeding images into a multimodal model that combines a vision encoder with a language model to generate captions and discuss visual content interactively.
Multimodal models support document understanding with embedded visuals by simultaneously processing text and image inputs to extract context across real-world interactive workflows.
Image-chat workflows require model selection and quantization options to accommodate different model sizes, ensuring proper handling of image inputs and multi-turn dialogue sessions.
Multi-turn dialogue handling is fully supported for visual question answering tasks, allowing continuous interactive sessions that maintain context across multiple image-grounded conversations.
Instruction-following for multimodal conversations depends on the integrated vision encoder and language model capabilities, requiring proper quantization and model size selection to handle complex image inputs effectively.
ovachieverEncode images and texts to compute cosine similarity for zero-shot classification and cross-modal retrieval.
Monjyu1101Enable multimodal image conversations and visual question answering with LLaVA models.
GregsGreyCodeEnable vision-language chat and image understanding with CLIP and Vicuna or LLaMA models.
Clay-HHKCombine CLIP vision encoders with large language models for visual question answering.
