Mini-InternVL: A Series of Multimodal Large Language Models (MLLMs) 1B to 4B, Achieving 90% of the Performance with Only 5% of the Parameters

**Introduction to Multimodal Large Language Models (MLLMs)** Multimodal large language models (MLLMs) are a new type of AI that combines visual and language processing. This allows them to understand and work with both images and text. They are particularly useful in fields like autonomous driving, medical imaging, and remote sensing, where analyzing both types of data is essential. **Challenges of MLLMs** While MLLMs are powerful, they have some challenges. They need a lot of computing power and can be difficult to use on devices with limited resources. Many of these models rely on general data from the internet, which may not perform well in specialized areas that require specific knowledge. **Current Limitations** Current MLLMs often use vision encoders to connect visual data with language. However, they struggle in specialized fields due to a lack of detailed visual knowledge. Adapting these models for specific tasks can be inefficient, especially for smaller devices. **Introducing Mini-InternVL** Researchers have created Mini-InternVL, a series of lightweight MLLMs with 1 to 4 billion parameters. This model aims to deliver 90% of the performance of larger models while using only 5% of the parameters, making it efficient for everyday devices. Mini-InternVL is suitable for tasks in autonomous driving, medical imaging, and remote sensing, all while requiring less computing power. **Key Features of Mini-InternVL** - **Robust Vision Encoder:** It uses a vision encoder called InternViT-300M, which helps it learn across different fields with fewer resources. - **Multiple Variants:** The series includes different versions (Mini-InternVL-1B, Mini-InternVL-2B, and Mini-InternVL-4B) to meet various needs. - **Two-Stage Training:** The model undergoes training to align language and images, improving its ability to adapt to real-world tasks. **Performance Achievements** Mini-InternVL has performed impressively on various tests, achieving up to 90% of the performance of larger models with only 5% of their parameters. For instance, Mini-InternVL-4B scored high on benchmarks, showing it can compete with more resource-heavy models in fields like autonomous driving, medical imaging, and remote sensing. **Conclusion** Mini-InternVL effectively reduces the high computing demands of multimodal models. It shows that smart design and training can lead to strong performance while using fewer resources. With its adaptable framework and powerful vision encoder, Mini-InternVL is a practical solution for specialized applications in resource-limited settings. **Transform Your Business with AI** To stay competitive, consider using Mini-InternVL for your business. Here’s how: 1. **Identify Automation Opportunities:** Look for areas in customer interactions that could benefit from AI. 2. **Define KPIs:** Make sure your AI projects have measurable goals. 3. **Select an AI Solution:** Choose tools that meet your needs and can be customized. 4. **Implement Gradually:** Start with a small project, collect data, and expand AI use wisely. For advice on managing AI KPIs, contact us. For more insights into AI, follow us on social media.