llava

Combine vision encoders with large language models to answer questions about images.

Updated Mar 13, 2026

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

Ready to run

npx skills add https://github.com/hochoa13/Asistente-Harlest --skill llava-hochoa13

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llava
Source: https://github.com/hochoa13/Asistente-Harlest/tree/main/skills/mlops/models/llava
Command: npx skills add https://github.com/hochoa13/Asistente-Harlest --skill llava-hochoa13

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 to understand and interpret visual information, bridging the gap between text and images for more intuitive and powerful AI interactions.

Core Features & Use Cases

Visual Question Answering (VQA): Ask questions about the content of an image.
Image Captioning: Generate detailed descriptions for images.
Multi-turn Image Chat: Engage in conversational interactions about images.
Use Case: Upload a photo of a complex scene and ask "What is the main activity happening here?" or "Describe the objects on the table."

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 perform visual question answering on an image using AI?▼

Visual question answering is achieved by combining large language models with vision encoders to process the image and generate a text response. This Skill facilitates VQA by interpreting visual information and answering specific questions about the image's content.

What is the best way to generate detailed image captions with AI?▼

Generating detailed image captions is best done by combining large language models with vision encoders to interpret the visual data. This approach processes the image and produces comprehensive textual descriptions of the scene and objects within it.

Can I use transformers and torch for multi-turn image-based conversations?▼

Yes, you can use transformers and torch to facilitate multi-turn image-based conversations. This Skill leverages these libraries alongside pillow to process images and maintain a conversational context for ongoing interactions about the visual content.

How does image understanding work when combining vision encoders with language models?▼

Image understanding works by passing visual data through a vision encoder and feeding the extracted features into a large language model. This integration bridges text and images, allowing the AI to interpret complex scenes and respond to queries.

Do I need pillow to process images for visual question answering tasks?▼

Yes, pillow is required for image processing tasks such as visual question answering. It works in conjunction with torch and transformers to load and format the visual data before the model inference and understanding processes begin.