What is InternVL3
InternVL3 is an open-source multimodal large language model (MLLM) from Shanghai Artificial Intelligence Laboratory, with outstanding multimodal perception and reasoning capabilities. The model series includes 7 versions of different sizes from 1B to 78B, and can process multiple types of information such as text, images, and videos simultaneously. InternVL3 adopts an innovative native multimodal pre-training method, integrating language and multimodal learning into the same pre-training stage, improving multimodal capabilities while further enhancing pure language capabilities. Through a mixed preference optimization algorithm and multimodal test-time enhancement, the model greatly improves its reasoning ability.
Main Features of InternVL3
- Multimodal Perception and Reasoning: InternVL3 can simultaneously process multiple types of information such as text, images, and videos, demonstrating outstanding multimodal perception and reasoning capabilities.
- Expanded Multimodal Capabilities: The model further expands multimodal capabilities, covering more application scenarios such as tool use, GUI agents, industrial image analysis, and 3D visual perception.
- Native Multimodal Pre-training: InternVL3 adopts an innovative native multimodal pre-training method, integrating language and multimodal learning into the same pre-training stage, improving multimodal capabilities while also enhancing pure language capabilities.
- Long-Context Understanding: By integrating variable visual position encoding (V2PE), InternVL3 performs better in long-context understanding.
- Efficient Deployment and Invocation: InternVL3 can be deployed as an OpenAI-compatible API through LMDeploy's api_server, and users can easily call the model through OpenAI's API interface.
Technical Principles of InternVL3
- Native Multimodal Pre-training: InternVL3 adopts an innovative native multimodal pre-training method, integrating language and visual learning into the same pre-training stage. Unlike traditional methods that first train a language model separately and then adapt it to multimodal tasks, InternVL3 directly mixes large-scale multimodal data (such as image-text and video-text sequences) with pure text data for training. The unified training approach enables the model to learn language and visual representations simultaneously, making it more efficient in handling vision-language tasks without the need for additional alignment modules.
- Supervised Fine-Tuning: In the fine-tuning stage, InternVL3 uses techniques such as random JPEG compression, square loss reweighting, and multimodal data packing. Compared withInternVL2.5, InternVL3 further expands high-quality training samples, covering multiple fields such as tool use, 3D scene understanding, and GUI operation. It enhances the model's robustness in complex scenarios.
- Mixed Preference Optimization: InternVL3 introduces MPO technology, which significantly improves the model's reasoning performance by combining preference loss, quality loss, and generation loss. MPO helps the model's output get closer to the true distribution by introducing additional supervision from positive and negative samples, reducing bias during reasoning.
- Dynamic Preprocessing and Multimodal Input Processing: InternVL3 supports dynamic preprocessing, and can dynamically adjust image size according to the aspect ratio of the input image and split it into multiple tiles to meet the model's input requirements. The model supports multiple multimodal dialogue scenarios such as multi-image input and video input, and can flexibly handle complex multimodal tasks.
InternVL3 Project Address
- HuggingFace Model Library：https://huggingface.co/OpenGVLab/InternVL3-78B
- Technical Paper：https://huggingface.co/papers/2504.10479
Application Scenarios of InternVL3
- Image and Video Understanding: InternVL3 can be used for tasks such as image classification, object detection, and video description generation. It can generate detailed descriptions based on input images or videos, serving content creation and automated editing.
- Intelligent Interaction and Tool Use: The model supports tool use and GUI agent capabilities. It can act as a graphical user interface (GUI) agent, following instructions to operate professional software on a computer or mobile phone.
- Industrial Image Analysis and 3D Visual Perception: InternVL3's multimodal capabilities extend to industrial image analysis and 3D visual perception. It can process complex industrial scene images and support tasks such as architectural drawing understanding and spatial perception reasoning.
- Intelligent Customer Service and Language Model Applications: Based on its powerful language generation capabilities, InternVL3 can be used to develop intelligent customer service systems, providing more efficient and accurate customer support.