cuuupid / qwen2-vl-2b

SOTA open-source model for chatting with videos and the newest model in the Qwen family

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This model runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

Qwen2-VL-2B-Instruct

Introduction

We’re excited to unveil Qwen2-VL, the latest iteration of our Qwen-VL model, representing nearly a year of innovation.

What’s New in Qwen2-VL?

Key Enhancements:

SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.

Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.

Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.

Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.

Model Architecture Updates:

Naive Dynamic Resolution: Unlike before, Qwen2-VL can handle arbitrary image resolutions, mapping them into a dynamic number of visual tokens, offering a more human-like visual processing experience.

**Multimodal Rotary Position Embedding (M-ROPE)**: Decomposes positional embedding into parts to capture 1D textual, 2D visual, and 3D video positional information, enhancing its multimodal processing capabilities.

We have three models with 2, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2-VL model. # Image Benchmarks

Benchmark InternVL2-8B MiniCPM-V 2.6 GPT-4o-mini Qwen2-VL-7B
MMMUval 51.8 49.8 60 54.1
DocVQAtest 91.6 90.8 - 94.5
InfoVQAtest 74.8 - - 76.5
ChartQAtest 83.3 - - 83.0
TextVQAval 77.4 80.1 - 84.3
OCRBench 794 852 785 845
MTVQA - - - 26.3
RealWorldQA 64.4 - - 70.1
MMEsum 2210.3 2348.4 2003.4 2326.8
MMBench-ENtest 81.7 - - 83.0
MMBench-CNtest 81.2 - - 80.5
MMBench-V1.1test 79.4 78.0 76.0 80.7
MMT-Benchtest - - - 63.7
MMStar 61.5 57.5 54.8 60.7
MMVetGPT-4-Turbo 54.2 60.0 66.9 62.0
HallBenchavg 45.2 48.1 46.1 50.6
MathVistatestmini 58.3 60.6 52.4 58.2
MathVision - - - 16.3
# Video Benchmarks
Benchmark Internvl2-8B LLaVA-OneVision-7B MiniCPM-V 2.6 Qwen2-VL-7B
MVBench 66.4 56.7 - 67.0
PerceptionTesttest - 57.1 - 62.3
EgoSchematest - 60.1 - 66.7
Video-MMEwo/w subs 54.0/56.9 58.2/- 60.9/63.6 63.3/69.0
# Limitations While Qwen2-VL are applicable to a wide range of visual tasks, it is equally important to understand its limitations. Here are some known restrictions: 1. Lack of Audio Support: The current model does **not comprehend audio information** within videos. 2. Data timeliness: Our image dataset is **updated until June 2023**, and information subsequent to this date may not be covered. 3. Constraints in Individuals and Intellectual Property (IP): The model's capacity to recognize specific individuals or IPs is limited, potentially failing to comprehensively cover all well-known personalities or brands. 4. Limited Capacity for Complex Instruction: When faced with intricate multi-step instructions, the model's understanding and execution capabilities require enhancement. 5. Insufficient Counting Accuracy: Particularly in complex scenes, the accuracy of object counting is not high, necessitating further improvements. 6. Weak Spatial Reasoning Skills: Especially in 3D spaces, the model's inference of object positional relationships is inadequate, making it difficult to precisely judge the relative positions of objects. These limitations serve as ongoing directions for model optimization and improvement, and we are committed to continually enhancing the model's performance and scope of application. # Citation If you find our work helpful, feel free to give us a cite. 
@article{Qwen2-VL,
  title={Qwen2-VL},
  author={Qwen team},
  year={2024}
}

@article{Qwen-VL,
  title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
  journal={arXiv preprint arXiv:2308.12966},
  year={2023}
}