Verified:GitHub

llava

Answer questions about images using a multimodal vision-language model.

Updated Feb 15, 2026

Search Tags:#fine-tuning #multimodal #vision-language #document-understanding #llava #visual-question-answering #image-chat

Ready to run

npx skills add https://github.com/ricable/mcai --skill llava-ricable

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llava
Source: https://github.com/ricable/mcai/tree/main/.agents/skills/llava
Command: npx skills add https://github.com/ricable/mcai --skill llava-ricable

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LLaVA helps people ask natural-language questions about images and receive detailed, context-aware answers without manual visual inspection.

Core Features & Use Cases

Visual question answering for single-image and multi-turn conversations.
Image captioning, scene understanding, object listing, and document image interpretation.
Practical for building multimodal chatbots, image analysis tools, and domain-specific assistants that reason over visual inputs.

Quick Start

Use the llava skill to analyze an attached image and answer a user question about what is visible in it.

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 multimodal model?▼

Visual question answering uses a multimodal vision-language model to generate context-aware answers about images. You attach an image and ask a natural-language question to receive detailed responses without manual inspection.

Can I use a vision-language model for multi-turn image chat and document understanding?▼

Yes, a vision-language model supports multi-turn image chat and document image interpretation. It processes image token formatting to handle conversational context and extract text from document images.

How does multimodal generation work with a CLIP-based vision encoder and LLaMA language model?▼

Multimodal generation works by routing images through a CLIP-based vision encoder and text through a LLaMA language model. The encoded visual features merge with language tokens for unified vision-language reasoning.

Do I need fine-tuning support to build a multimodal chatbot for domain-specific scene understanding?▼

Fine-tuning support is optional for building multimodal chatbots. You can use base models for general scene understanding, but fine-tuning improves accuracy for domain-specific visual analysis workflows.

What are the limitations of using quantization for image captioning in multimodal vision-language models?▼

Quantization reduces memory usage during image captioning but may degrade output precision. Optional quantization support helps deploy multimodal vision-language models in resource-constrained environments.