llava

Combine CLIP vision encoders with Vicuna/LLaMA language models for vision-language applications.

4|Updated Apr 19, 2026

Search Tags:#chatbot #image analysis #vision-language #VQA #LLaVA

Ready to run

npx skills add https://github.com/ragnarokhaa/hermes --skill llava-ragnarokhaa

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llava
Source: https://github.com/ragnarokhaa/hermes/tree/main/hermes-cerul-tech-news-package/hermes-cerul-tech-news-package/hermes-agent/optional-skills/mlops/llava
Command: npx skills add https://github.com/ragnarokhaa/hermes --skill llava-ragnarokhaa

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 the creation of advanced vision-language applications, such as conversational image analysis, visual question answering, and visual instruction following, by combining the power of CLIP vision encoders with Vicuna/LLaMA language models.

Core Features & Use Cases

Visual Instruction Tuning: Enables the tuning of visual instructions for image-based conversations.
Image-based Conversations: Supports multi-turn image chat, allowing for interactive conversations around images.
Use Case: Build a chatbot that can engage in a conversation about a user-uploaded image, providing insights and answering questions.

Quick Start

To start a conversation with LLaVA, upload an image and ask a question about it.

Frequently Asked Questions about llava

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

FAQPage Schema

How do I build a chatbot that can analyze and answer questions about an uploaded image?▼

To build an image analysis chatbot, you need a vision-language application that combines CLIP vision encoders with Vicuna or LLaMA language models. This approach supports multi-turn image chat and visual question answering. You must upload an image and ask a question to start the conversation.

What is visual instruction tuning for image-based conversations?▼

Visual instruction tuning is the process of training models to follow instructions based on images. It enables advanced vision-language applications to conduct interactive, multi-turn conversations around user-uploaded images, providing insights and answering specific questions.

Can I use transformers and torch to create visual question answering applications?▼

Yes, you can use transformers and torch alongside pillow to create visual question answering applications. These dependencies are required to combine CLIP vision encoders with Vicuna or LLaMA language models for image-based instruction following and conversational image analysis.

Does LLaVA support multi-turn image chat for interactive image understanding?▼

Yes, LLaVA supports multi-turn image chat for interactive image understanding. It enables vision-language applications to maintain conversational context, allowing users to ask follow-up questions about an uploaded image and receive continuous insights.

What's the best way to combine CLIP vision encoders with Vicuna language models?▼

The best way to combine CLIP vision encoders with Vicuna language models is through visual instruction tuning. This method creates advanced vision-language applications capable of image-based instruction following and conversational image analysis using transformers, torch, and pillow.

Do I need pillow to run vision-language models for image chatbots?▼

Yes, you need pillow along with transformers and torch to run vision-language models for image chatbots. Pillow handles image processing inputs required by the CLIP vision encoders before the Vicuna or LLaMA language models generate conversational responses.