llava

Enable visual question answering and image captioning with multimodal AI models.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/kwasi-cpu/hermes-agent --skill llava
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: llava
Source: https://github.com/kwasi-cpu/hermes-agent/tree/main/skills/mlops/models/llava
Command: npx skills add https://github.com/kwasi-cpu/hermes-agent --skill llava

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables AI to understand and interpret images, allowing for visual question answering and image-based conversations.

Core Features & Use Cases

  • Visual Question Answering (VQA): Ask questions about the content of an image.
  • Image Captioning: Generate detailed descriptions of images.
  • Multimodal Chatbots: Build conversational agents that can process and discuss images.
  • Use Case: Upload a picture of a product and ask "What material is this made of?" or "Describe the scene in this photo."

Quick Start

Use the llava skill to describe the image provided.

Frequently Asked Questions about llava

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

FAQPage Schema
How do I enable visual question answering with an image and a text prompt?

Visual question answering is enabled by processing an image using pillow and passing it with a text prompt to a multimodal vision-language model. This allows the AI to interpret image content and provide conversational responses.

Do I need PyTorch and transformers to build a multimodal chatbot that discusses images?

Yes, building a multimodal chatbot requires torch and transformers to run the vision-language models, along with pillow for image processing. These libraries handle the model inference and image manipulation necessary for image-based conversations.

What is the best way to generate detailed descriptions of images using AI?

Generating detailed image descriptions is best achieved through image captioning with a multimodal AI model. By integrating a vision-language model, the system analyzes visual content and outputs accurate textual descriptions of the scene.

Can I use this multimodal AI approach to identify materials in product photos?

Yes, you can use multimodal AI to identify materials in product photos. By uploading the image and asking a specific question like "What material is this made of?", the vision-language model analyzes the visual data to provide an answer.

Are there limitations when using local libraries for image understanding tasks?

Limitations for image understanding tasks depend on the specific vision-language models supported by transformers and torch. Processing requires adequate hardware for model inference, and the accuracy of visual interpretation is bounded by the underlying model's capabilities.