llava

Enable multimodal visual instruction tuning and image-based conversational analysis.

2|1|Updated Jul 14, 2026

Search Tags:#multimodal #computer vision #conversational ai #vision-language #vqa #llava

Ready to run

npx skills add https://github.com/heysuhas/hermes_cli --skill llava-heysuhas

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the difficulty of interpreting and interacting with visual content by enabling AI to see, describe, and answer questions about images.

Core Features & Use Cases

Visual Question Answering: Ask specific questions about the contents of an image.
Multi-turn Conversations: Engage in back-and-forth dialogue about visual scenes.
Use Case: Use this skill to analyze a series of product photos to generate detailed descriptions or to identify specific objects within a complex scene for inventory management.

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 answer questions about image contents using visual question answering?▼

Visual question answering is enabled through multimodal visual instruction tuning and image-based conversational analysis. This skill supports tasks ranging from visual question answering to detailed image captioning and document understanding by processing image-text pairs for inference.

What is multimodal visual instruction tuning for image analysis?▼

Multimodal visual instruction tuning enables AI to see, describe, and answer questions about images by integrating vision-language models like CLIP and Vicuna to process image-text pairs for inference and detailed image captioning.

Do I need PyTorch and Transformers to run image-based conversational analysis?▼

Yes, image-based conversational analysis requires the torch, transformers, and pillow dependencies. These frameworks support the integration with vision-language models like CLIP and Vicuna needed to process image-text pairs for inference.

Can I use conversational AI to identify specific objects within a complex scene?▼

Conversational AI can identify specific objects within a complex scene through multi-turn dialogue and visual question answering. You can analyze product photos to generate detailed descriptions or identify objects for inventory management purposes.

What are the limitations of using vision-language models for document understanding?▼

Vision-language models for document understanding are limited by their dependency on integrating with specific models like CLIP and Vicuna. The effectiveness of interpreting visual content depends on the quality of image-text pair processing for inference.