Internvit 6B 448px V1 2

🚀 InternViT-6B-448px-V1-2

InternViT-6B-448px-V1-2是全新发布的权重版本。在InternVL 1.2更新中，涉及对InternViT-6B模型的持续预训练。具体而言，将InternViT-6B的分辨率从224提升至448，并与[Nous-Hermes-2-Yi-34B]((https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)进行集成。

[📂 GitHub] [📜 InternVL 1.0] [📜 InternVL 1.5] [📜 Mini-InternVL] [📜 InternVL 2.5]

[🆕 Blog] [🗨️ Chat Demo] [🤗 HF Demo] [🚀 Quick Start] [📖 Documents]

✨ 主要特性

• 分辨率提升：将InternViT-6B的分辨率从224提高到448，增强了模型处理高分辨率图像的能力。
• 集成优化：与[Nous-Hermes-2-Yi-34B]((https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)集成，提升了模型的性能。
• 数据增强：在通用图像描述数据集的基础上，加入额外的OCR数据，提升了模型的OCR能力。
• 参数优化：通过丢弃最后3个块，将模型参数从59亿减少到55亿，节省了GPU内存。

📦 安装指南

文档未提及具体安装步骤，暂不提供。

💻 使用示例

基础用法

import torch
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor

model = AutoModel.from_pretrained(
    'OpenGVLab/InternViT-6B-448px-V1-2',
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True).cuda().eval()

image = Image.open('./examples/image1.jpg').convert('RGB')

image_processor = CLIPImageProcessor.from_pretrained('OpenGVLab/InternViT-6B-448px-V1-2')

pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()

outputs = model(pixel_values)

📚 详细文档

模型详情

注意事项

InternViT-6B原本有48个块，经实验发现，使用倒数第4个块之后的输出对多模态大语言模型（MLLM）效果最佳。为了方便使用和节省GPU内存，直接丢弃了最后3个块。现在，模型仅剩下45个块，参数数量从59亿减少到55亿。因此，如果要基于此模型构建MLLM，请使用最后一层的特征。

⚠️ 重要提示

根据经验，InternViT V2.5系列更适合用于构建多模态大语言模型，而非传统的计算机视觉任务。

📄 许可证

本项目采用MIT许可证发布。

🔖 引用

如果您在研究中发现本项目有用，请考虑引用以下文献：

@article{chen2024expanding,
  title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
  author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
  journal={arXiv preprint arXiv:2412.05271},
  year={2024}
}
@article{gao2024mini,
  title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
  author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
  journal={arXiv preprint arXiv:2410.16261},
  year={2024}
}
@article{chen2024far,
  title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
  author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
  journal={arXiv preprint arXiv:2404.16821},
  year={2024}
}
@inproceedings{chen2024internvl,
  title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},
  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={24185--24198},
  year={2024}
}