What is MiniCPM-V 4.5
MiniCPM-V 4.5 is an on-device multimodal model launched by ModelBest, with 8B parameters. The model performs excellently in multiple domains such as images, video, and OCR, and has achieved a breakthrough especially in high-frame-rate video understanding, capable of processing high-refresh-rate video and accurately recognizing content. The model supports a hybrid reasoning mode, balancing performance and response speed. MiniCPM-V 4.5 is deployment-friendly on device, with low VRAM usage and fast inference speed, suitable for applications on devices such as in-vehicle systems and robots, setting a new benchmark for the development of on-device AI.
Main Features of MiniCPM-V 4.5
- High-Frame-Rate Video Understanding: Supports processing high-refresh-rate video, accurately recognizing rapidly changing screen content, for example, recognizing the rapidly changing text on each sheet of paper in a 3-second paper-flipping video.
- Single-Image Understanding: Performs excellently in image understanding, accurately recognizing and analyzing information such as objects and scenes in images, with performance surpassing multiple large closed-source models.
- Complex Document Recognition: Efficiently recognizes and parses information such as text and tables in complex documents, including handwritten text and structured table extraction.
- OCR Function: Possesses powerful optical character recognition capabilities, accurately recognizing text content in images, supporting multiple fonts and layouts.
- Hybrid Reasoning Mode: Supports "long thinking" and "short thinking" modes, capable of in-depth analysis and fast responses, meeting the needs of different scenarios.
Technical Principles of MiniCPM-V 4.5
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3D-Resampler High-Density Video Compression: Extends the model structure from 2D-Resampler to 3D-Resampler, performing high-density compression on 3D video clips, enabling the reception of more video frames without increasing inference overhead, achieving a 96x visual compression rate and better understanding of dynamic processes.
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Unified OCR and Knowledge Reasoning Learning: By controlling the "visibility of text information" in images, seamlessly switches between OCR and knowledge learning modes, achieving effective integration of OCR and knowledge learning, and enhancing the model's text recognition and knowledge reasoning capabilities.
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General-Domain Hybrid Reasoning Reinforcement Learning: With the help of RLPR technology, obtains high-quality reward signals from general-domain multimodal reasoning data, and uses a hybrid reasoning reinforcement learning scheme to simultaneously improve the model's performance in both regular mode and deep thinking mode.
Project Addresses of MiniCPM-V 4.5
- GitHub Repository：https://github.com/OpenBMB/MiniCPM-V
- HuggingFace Model Library：https://huggingface.co/openbmb/MiniCPM-V-4_5
- Online Experience Demo：http://101.126.42.235:30910/
Application Scenarios of MiniCPM-V 4.5
- Intelligent Driving: Recognizes road signs, traffic signals, and pedestrians in real time, providing drivers with more accurate road condition information and significantly improving driving safety and convenience.
- Intelligent Robots: In home or industrial environments, helps robots perceive their surroundings in real time, recognize objects and human actions, and make more reasonable interactive behaviors.
- Smart Home: Used in home security systems to monitor the home environment in real time, identify abnormal behavior and issue alerts promptly, and automatically adjust home devices based on ambient light and personnel location.
- Education: Students can have the model recognize and parse charts, formulas, etc. in textbooks by taking photos or uploading images, obtain detailed explanations and tutoring, and improve learning efficiency.
- Healthcare: In the medical field, quickly identifying and analyzing abnormal areas in medical images such as X-rays and CT scans, assisting doctors in making more efficient and accurate diagnoses.