What are the primary use cases for this skill?

It is used for visual question answering, image captioning, document understanding, and building conversational agents that can 'see' and interpret visual data.

What hardware is required for this skill?

A CUDA-compatible NVIDIA GPU is highly recommended for inference. While CPU inference is possible, it is significantly slower than GPU-accelerated processing.

LLaVA (Large Language and Vision Assistant) is an open-source multimodal model that connects vision encoders with language models to understand and discuss images.

Can I run LLaVA on a local machine?

Yes, LLaVA supports 4-bit and 8-bit quantization, allowing the 7B model to run on consumer GPUs with as little as 4GB-8GB of VRAM.

How does LLaVA compare to GPT-4V?

While GPT-4V is a proprietary API, LLaVA provides similar visual instruction-following capabilities in an open-source framework that can be self-hosted and fine-tuned.

LLaVA Multimodal Vision Assistant

Name: LLaVA Multimodal Vision Assistant
Author: Orchestra-Research

byOrchestra-Research

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3,983

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Data Science & ML

Integrates LLaVA to enable sophisticated visual instruction following and multi-turn conversational image understanding.

LLaVA (Large Language and Vision Assistant) bridges the gap between vision and language by combining a CLIP vision encoder with LLaMA/Vicuna language models. This skill empowers AI agents to perform complex visual tasks like scene description, visual question answering (VQA), and document analysis. It serves as a powerful open-source alternative to proprietary vision models, offering flexibility through various model sizes (7B-34B) and quantization options for efficient local deployment and research.

Key Features

01Multimodal instruction tuning for conversational image analysis

02Visual Question Answering (VQA) and detailed image captioning

03Quantization support (4-bit and 8-bit) for reduced VRAM usage

04Seamless integration with CLIP and Vicuna/LLaMA architectures

053,983 GitHub stars

06Support for multi-turn image-based dialogue and context retention

Use Cases

01Automating metadata generation and descriptive captioning for image libraries

02Extracting information and answering questions from complex visual documents

03Building open-source vision-language chatbots and virtual assistants

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills llava

For use in Claude.ai and ChatGPT

Download Skill