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llava

Generates grounded conversational responses from user-provided images using CLIP and LLaMA/Vicuna-style language pipelines.

Updated May 2, 2026

Search Tags:#multimodal #instruction tuning #CLIP #vision-language #vqa #image chat

Ready to run

npx skills add https://github.com/qcmuu/AI-Research-Skills --skill llava-qcmuu

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the problem of translating visual content in images into helpful, conversational answers without manually writing complex vision pipelines.

Core Features & Use Cases

Multimodal image chat (VQA): Ask questions about what’s in an image and get context-aware answers suitable for assistants and support bots.
Image instruction following: Perform visual reasoning for tasks like describing scenes, answering questions about objects, or summarizing visual documents.
Quick deployment options: Use the provided Python/CLI entry points and Gradio web UI to turn the model into an interactive app.

Example use case: upload an image of a lab diagram and ask what each labeled part does to support study notes or internal documentation review.

Quick Start

Load a pretrained LLaVA checkpoint and send an image plus a question to get an image-grounded response in one turn of conversational chat.

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 with an uploaded image?▼

Visual question answering is achieved by uploading an image and asking a question, which generates grounded conversational responses based on the visual content. This supports multimodal instruction following for assistant bots and document understanding scenarios.

What is multimodal instruction tuning for image chat?▼

Multimodal instruction tuning for image chat is a mechanism that aligns visual content with language generation. It uses a CLIP-style vision encoder and a LLaMA/Vicuna-style language model to produce text conditioned on image tokens.

Can I use a CLIP vision encoder with a Vicuna language model for image description?▼

Yes, you can use a CLIP-style vision encoder with a Vicuna-style language model to perform image description. This pipeline requires transformers and image preprocessing to translate visual content into conversational answers.

How do I start multi-turn image chat with a pretrained checkpoint?▼

To start multi-turn image chat, load a pretrained checkpoint and send an image with a question. You can use the provided Python/CLI entry points or Gradio web UI to turn the model into an interactive app.

Does this vision-language model support document understanding scenarios?▼

Yes, the vision-language model supports document understanding scenarios by answering questions about objects and summarizing visual documents. It translates visual content into helpful answers without writing complex vision pipelines.