迷你版多模态模型用5%参数达到90%性能，适合边缘设备部署。
Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance
- 仅用1B~4B参数，实现主流大模型90%的性能
- 在自动驾驶、医疗影像等任务中超越专用模型
- 统一适配框架让小模型也能灵活迁移应用
多模态大语言模型在视觉-语言任务中表现优异，但其庞大的规模和高昂的计算成本限制了在消费级GPU或边缘设备上的训练与部署。本文提出Mini-InternVL系列模型，参数量为1B至4B，仅需5%的参数即可达到原模型90%的性能，显著提升效率与实用性。为促进模型应用，我们开发了统一的适应框架，使Mini-InternVL可在自动驾驶、医学图像、遥感等下游任务中实现跨领域迁移，并优于专门训练的模型。该研究为高效多模态模型的发展提供了重要参考。代码已开源：https://github.com/OpenGVLab/InternVL。
原文摘要 · Abstract (English)
Multimodal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a broad spectrum of domains. However, the large model scale and associated high computational costs pose significant challenges for training and deploying MLLMs on consumer-grade GPUs or edge devices, thereby hindering their widespread application. In this work, we introduce Mini-InternVL, a series of MLLMs with parameters ranging from 1B to 4B, which achieves 90% of the performance with only 5% of the parameters. This significant improvement in efficiency and effectiveness makes our models more accessible and applicable in various real-world scenarios. To further promote the adoption of our models, we develop a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks, including autonomous driving, medical images, and remote sensing. We believe that our study can provide valuable insights and resources to advance the development of efficient and effective MLLMs. Code is available at https://github.com/OpenGVLab/InternVL.
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