vip-llava-7b Open-source Multimodal Chatbot - Free Deployment with Support for Image and Region Instruction Dialogue

Vip Llava 7b

Developed by mucai

ViP-LLaVA is an open-source multimodal chatbot, fine-tuned on LLaMA/Vicuna with image and region-level instruction data.

Text-to-Image

Transformers

#Regional-level visual understanding #Multimodal instruction fine-tuning #Visual prompt annotation

Downloads 66.75k

Release Time : 12/3/2023

Model Overview

ViP-LLaVA is an autoregressive language model based on the Transformer architecture, primarily used for research in large multimodal models and chatbots.

Model Features

Multimodal capability

Combines visual and language understanding to process both image and text inputs

Regional-level visual understanding

Capable of understanding and reasoning about specific regions in images

Open-source accessibility

Model is open-source and available for research and development

High performance

Achieves state-of-the-art performance on multiple regional-level benchmarks

Model Capabilities

Image understanding

Regional-level visual reasoning

Multimodal dialogue

Image caption generation

Use Cases

Academic research

Multimodal model research

Used to study the performance and capabilities of vision-language models

Excellent performance on benchmarks like RegionBench

Computer vision research

Used to study image understanding and regional-level visual reasoning

Application development

Intelligent chatbot

Develop dialogue systems capable of understanding image content

Image analysis tool

Develop tools capable of analyzing specific regions in images

🚀 ViP-LLaVA Model Card

ViP-LLaVA is an open - source chatbot. It is fine - tuned on LLaMA/Vicuna with both image - level and region - level instruction data annotated by visual prompts. This model offers great potential for research in large multimodal models and chatbots.

🚀 Quick Start

This section provides a quick overview of the ViP-LLaVA model. For more detailed information, please refer to the subsequent sections.

✨ Features

ViP-LLaVA is an auto - regressive language model based on the transformer architecture.
It is trained on a diverse set of datasets, enabling it to handle various multimodal tasks.
Achieves state - of - the - art performance in 4 academic region - level benchmarks and the newly proposed RegionBench.

📦 Installation

No installation steps were provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples were provided in the original document, so this section is skipped.

📚 Documentation

Model Details

Property	Details
Model Type	ViP-LLaVA is an open - source chatbot trained by fine - tuning LLaMA/Vicuna on both image level instruction data and region - level instruction data annotated with visual prompts. It is an auto - regressive language model, based on the transformer architecture.
Model Date	ViP-LLaVA - 7B was trained in November 2023. Paper
Paper or Resources for More Information	https://vip-llava.github.io/

Intended Use

Property	Details
Primary Intended Uses	The primary use of ViP-LLaVA is research on large multimodal models and chatbots.
Primary Intended Users	The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Training Dataset

558K filtered image - text pairs from LAION/CC/SBU, captioned by BLIP.
665K image level instruction data from LLaVA - 1.5.
520K image - text pairs marked with visual prompts.
13K region - level instruction data generated from GPT - 4V.

Evaluation Dataset

ViP-LLaVA achieves state - of - the - art performance in 4 academic region - level benchmarks and our newly proposed RegionBench.

🔧 Technical Details

No specific technical implementation details (>50 words) were provided in the original document, so this section is skipped.

📄 License

Where to send questions or comments about the model: [https://github.com/mu - cai/ViP-LLaVA/issues](https://github.com/mu - cai/ViP-LLaVA/issues)

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