llava

Name: llava
Availability: InStock
Author: zangjeicy

Integrate CLIP vision encoder with LLaMA models for visual question answering.

Updated Jul 13, 2026

Search Tags:#multimodal #computer vision #image analysis #vision-language #vqa #llava

Ready to run

npx skills add https://github.com/zangjeicy/Hermes --skill llava-zangjeicy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llava
Source: https://github.com/zangjeicy/Hermes/tree/main/optional-skills/mlops/llava
Command: npx skills add https://github.com/zangjeicy/Hermes --skill llava-zangjeicy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill bridges the gap between text-based AI and visual perception, allowing users to perform complex image analysis and visual question answering without needing specialized computer vision expertise.

Core Features & Use Cases

Visual Question Answering: Ask natural language questions about the contents of an image.
Multi-turn Image Chat: Engage in continuous, context-aware conversations about visual media.
Use Case: Use this tool to analyze a series of product photos to identify defects, or to have the AI describe and summarize the contents of complex technical diagrams or documents.

Quick Start

Use the llava skill to analyze the provided image and describe the objects present in the scene.

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 language model?▼

Visual question answering integrates a CLIP vision encoder with LLaMA-based language models to process image and text inputs. You can ask natural language questions about image contents and receive context-aware answers without specialized computer vision expertise.

Can I have multi-turn conversations about visual media with an AI?▼

Multi-turn image chat enables continuous, context-aware conversations about visual media by integrating vision encoders with language models. You can engage in ongoing dialogues to analyze product photos, describe technical diagrams, or summarize document contents iteratively.

Do I need torch and transformers to run multimodal inference workflows?▼

Multimodal inference workflows require torch, transformers, and pillow to execute visual reasoning tasks. These dependencies support the integration of CLIP vision encoders with LLaMA-based language models for image analysis and visual instruction tuning.

What is visual instruction tuning for image-based conversational AI?▼

Visual instruction tuning bridges text-based AI and visual perception by training models on image-text pairs to perform complex image analysis. It enables capabilities like image captioning, document understanding, and visual question answering across various model scales.

Does this approach work for document understanding and image captioning tasks?▼

Document understanding and image captioning are supported through the integration of CLIP vision encoders with LLaMA-based language models. You can analyze complex technical diagrams, describe scene objects, and summarize document contents using natural language instructions.