Answer visual questions and caption images through conversational prompts.
npx skills add https://github.com/zerwiz/wayofpi --skill llava-zerwiz
Please help me install this Agent Skill. Skill: llava Source: https://github.com/zerwiz/wayofpi/tree/main/.hermes/hermes-agent/optional-skills/mlops/llava Command: npx skills add https://github.com/zerwiz/wayofpi --skill llava-zerwiz
LLaVA helps you understand images with natural language, removing the friction of switching between visual inspection and manual annotation. It is designed for conversational image analysis, visual question answering, captioning, and document understanding.
Ask the llava skill to analyze an image and answer a specific question about what it contains.
High-intent search queries and answers about installing and using this skill.
Conversational image understanding lets you ask questions about an image using natural language prompts. You can perform visual question answering, identify objects, and describe scenes without manually switching between visual inspection and annotation tools.
Yes, multimodal chat supports multi-turn conversations about a single image while preserving context. This allows you to iteratively ask follow-up questions, extract document details, or refine a design review across CLI, web, or programmatic interfaces.
Visual question answering and image captioning require a CLIP-based vision encoder paired with a Vicuna or LLaMA language model. Optional quantization and fine-tuning support are available to optimize GPU-based inference and training for different hardware limits.
Yes, document understanding is a core feature of this vision-language approach. It extracts meaning from documents, analyzes screenshots, and identifies UI elements, bridging visual inspection and text extraction without manual annotation.
You can load pretrained checkpoints to run CLI or web demos for multimodal chatbot workflows. Applying optional quantization helps adapt the language model and vision encoder to fit specific GPU-based inference and hardware constraints.
Apply quantization or fine-tuning when hardware limits constrain GPU-based inference for image captioning. These model operations optimize the Vicuna or LLaMA checkpoints to run efficiently across CLI, web, or programmatic interfaces without exceeding memory constraints.
sheawinklerEnable multimodal vision-language reasoning and conversation with images.
davpatel605-beepAnalyze images and generate conversational responses using LLaVA vision-language models.
Supporter09Analyze images for visual question answering and multi-turn multimodal chat.
ricableAnswer questions about images using a multimodal vision-language model.
