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llava

Combine CLIP vision encoder with Vicuna/LLaMA language models for image-based conversations.

1|1|Updated May 9, 2026
Search Tags:#conversational AI#chatbot#vision-language#image understanding#visual question answering
One-click install
npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill llava-ldzhhxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llava
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/optional-skills/mlops/llava
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill llava-ldzhhxx

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

LLaVA addresses the challenge of visual instruction tuning and image-based conversations, providing a tool for building vision-language chatbots and performing image understanding tasks.

Core Features & Use Cases

  • Visual Instruction Tuning: Teach models to follow visual instructions.
  • Image-based Conversations: Support multi-turn image chat and visual question answering.
  • Use Case: Create a vision-language chatbot that can understand and respond to visual instructions, or develop an application for image description and captioning.

Quick Start

Use the llava skill to generate a response to the image 'example.jpg' with the prompt 'What is in this image?'.

Frequently Asked Questions about llava

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

FAQPage Schema
How do I build a vision-language chatbot that answers questions about an image?▼

To build a vision-language chatbot, you can use this platform to combine a CLIP vision encoder with Vicuna/LLaMA models. It supports multi-turn image chat and visual question answering, allowing your application to follow visual instructions and understand image content.

What is visual instruction tuning and when do I need it for image understanding tasks?▼

Visual instruction tuning teaches language models to follow visual instructions by combining vision encoders with text models. You need it to develop applications for image captioning or to create chatbots that understand and respond accurately to visual inputs.

Can I use transformers and torch to perform multi-turn image chat with Vicuna models?▼

Yes, this platform combines CLIP vision encoders with Vicuna/LLaMA language models using transformers, torch, and pillow. This integration directly supports performing multi-turn image chat and visual question answering within your existing environment.

What's the best way to start generating image descriptions with a CLIP vision encoder?▼

The best way to generate image descriptions is to provide an input image and a text prompt like 'What is in this image?'. The system uses the combined CLIP and Vicuna/LLaMA architecture to process the visual content and generate a descriptive response.

Do I need pillow to perform visual question answering with LLaMA models?▼

Yes, pillow is required alongside transformers and torch to process input images. These dependencies are necessary to handle the image loading and preprocessing before the LLaMA language model generates answers to your visual questions.

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