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Pixelreasoner RL V1
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Pixelreasoner RL V1

Developed by TIGER-Lab
PixelReasoner is a vision-language model based on Qwen2.5-VL-7B-Instruct, trained with curiosity-driven reinforcement learning, focusing on image-text-to-text tasks.
Image-to-Text
Transformers
 EnglishOpen Source License:Apache-2.0#Visual Reasoning Reinforcement Learning#Multimodal Instruction Understanding#High-Precision Image Captioning
Downloads 112
Release Time : 5/18/2025

Model Overview

This model is primarily used for tasks involving interaction between images and text, capable of understanding image content and generating relevant textual descriptions or answering image-based questions.

Model Features

Curiosity-Driven Reinforcement Learning
Trained using the curiosity-driven reinforcement learning method described in the paper, improving the model's learning efficiency and performance.
Multimodal Capabilities
Combines visual and language processing abilities to understand and generate text related to images.
Efficient Inference
Provides inference code based on vllm and hf.generate(), supporting efficient deployment and usage.

Model Capabilities

Image Understanding
Text Generation
Multimodal Interaction

Use Cases

Image Caption Generation
Automatic Image Tagging
Generates detailed textual descriptions for images, suitable for content management and retrieval.
Visual Question Answering
Image-Based Q&A System
Answers user questions about image content, applicable in fields like education and healthcare.
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