Visionreasoner 7B

🚀 VisionReasoner-7B

VisionReasoner-7B is a model that combines a reasoning model and a segmentation model to handle image - text - to - text tasks.

Paper

Code: https://github.com/dvlab-research/VisionReasoner

Project page: https://github.com/dvlab-research/VisionReasoner

🚀 Quick Start

VisionReasoner-7B is a powerful model for image - text - to - text tasks. It has a unique decoupled architecture that combines a reasoning model and a segmentation model.

✨ Features

• Decoupled Architecture: It consists of a reasoning model and a segmentation model. The reasoning model interprets user intentions, generates explicit reasoning chains, and produces positional prompts. The segmentation model then uses these prompts to generate pixel - level masks.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# load model
model = AutoModelForCausalLM.from_pretrained("Ricky06662/VisionReasoner-7B")
tokenizer = AutoTokenizer.from_pretrained("Ricky06662/VisionReasoner-7B")

📚 Documentation

Model Information

📄 License

This project is licensed under the Apache - 2.0 license.