llava

Combine large language models with vision encoders for image-based conversations and question answering.

1|Updated Feb 21, 2026

Search Tags:#multimodal #conversational ai #vision-language #image understanding #vqa #llava

Ready to run

npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill llava-tianhao909

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llava
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/18-multimodal/llava
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill llava-tianhao909

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, pillow, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables AI agents to understand and interact with visual information, bridging the gap between text-based AI and the visual world.

Core Features & Use Cases

Visual Question Answering: Ask questions about the content of an image.
Image Captioning: Generate detailed descriptions of images.
Conversational Image Analysis: Engage in multi-turn dialogues about an image.
Use Case: Upload a photo of a park and ask "What breed is the dog playing fetch?" to get a specific answer.

Quick Start

Use the llava skill to describe the attached image file named 'park_scene.jpg'.

Frequently Asked Questions about llava

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform visual question answering on an image using a large language model?▼

Visual question answering combines vision encoders with large language models to analyze image content and answer specific questions. This Skill enables multimodal understanding by tuning models to process visual instructions and respond accurately.

Can I have multi-turn conversations about an image to extract detailed information?▼

Yes, you can engage in multi-turn image chat. The Skill supports conversational image analysis, allowing you to ask follow-up questions about a specific image and receive detailed answers based on the visual context.

What is the best way to generate detailed descriptions from images using conversational AI?▼

To generate detailed image descriptions, use a multimodal model that supports visual instruction tuning. This approach bridges text-based AI and the visual world, producing comprehensive captions and analysis of uploaded images.

Does this multimodal approach work with transformers and torch for complex visual reasoning?▼

Yes, this approach is built on transformers and torch. It leverages these frameworks to perform complex visual reasoning tasks, combining vision encoders with language models to process and understand visual information.

How do I build a vision-language chatbot that understands uploaded photos?▼

Build a vision-language chatbot by integrating a vision encoder with a large language model. This Skill provides the necessary multimodal understanding to process uploaded photos and respond to user queries with specific visual details.

What are the limitations of using visual instruction tuning for image understanding?▼

Visual instruction tuning for image understanding requires significant computational resources and depends on the quality of the input image. Complex visual reasoning tasks may be limited by the vision encoder's ability to accurately capture fine details.