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MiniCPM-V 4.5 - Wallfacer Intelligence's open-source edge multimodal model

MiniCPM-V 4.5 is an edge-side multimodal model launched by Facewall Intelligence, featuring 8-bit parameters. The model excels in multiple fields including image processing, video processing, and OCR, achieving breakthroughs particularly in high refresh rate video understanding, capable of handling high refresh rate video...

What is MiniCPM-V 4.5?

MiniCPM-V 4.5 is an edge-side multimodal model launched by Facewall Intelligence, featuring 8B parameters. The model excels in multiple fields including image, video, and OCR, achieving breakthroughs particularly in high refresh rate video understanding, capable of processing high refresh rate videos and accurately recognizing content. The model supports a hybrid inference mode, balancing performance and response speed. MiniCPM-V 4.5 is edge-side deployment-friendly, with low memory usage and fast inference speed, making it suitable for applications in vehicles, robots, and other devices, setting a new benchmark for edge AI development.

Main functions of MiniCPM-V 4.5

  • High refresh rate video understandingIt supports processing high refresh rate videos and accurately identifies rapidly changing screen content, such as recognizing the rapidly changing text on each sheet of paper in a 3-second paper-flipping video.
  • Single image understandingIt excels in image understanding, accurately identifying and analyzing objects, scenes, and other information in images, outperforming many large closed-source models.
  • Complex document recognitionIt can efficiently recognize and parse information such as text and tables in complex documents, including handwritten text and structured table extraction.
  • OCR functionIt possesses powerful optical character recognition capabilities, accurately recognizing text content in images and supporting various fonts and layouts.
  • Hybrid reasoning modeIt supports both "long thinking" and "short thinking" modes, enabling in-depth analysis and rapid response to meet the needs of different scenarios.

Technical principles of MiniCPM-V 4.5

  • 3D-Resampler High-Density Video CompressionThe model structure is extended from 2D-Resampler to 3D-Resampler, and high-density compression is performed on 3D video clips to receive more video frames without changing the inference overhead, achieving a visual compression rate of 96 times and better understanding of dynamic processes.
  • Unified OCR and Knowledge Reasoning LearningBy controlling the "visibility of text information" in the image, it seamlessly switches between OCR and knowledge learning modes, effectively integrating OCR and knowledge learning to improve the model's text recognition and knowledge reasoning capabilities.
  • General domain hybrid reasoning reinforcement learningBy leveraging RLPR technology, high-quality reward signals are obtained from general-domain multimodal inference data, and a hybrid inference reinforcement learning scheme is used to improve the model's performance in both regular and deep thinking modes.

MiniCPM-V 4.5 project address

  • GitHub repositoryhttps://github.com/OpenBMB/MiniCPM-V
  • HuggingFace model libraryhttps://huggingface.co/openbmb/MiniCPM-V-4_5
  • Experience the demo online:http://101.126.42.235:30910/

Application scenarios of MiniCPM-V 4.5

  • Intelligent drivingIt can identify 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 robotsIn home or industrial environments, it helps robots perceive their surroundings in real time, recognize objects and human movements, and make more reasonable interactive behaviors.
  • Smart HomeUsed in home security systems, it monitors the home environment in real time, identifies abnormal behavior and issues alarms promptly, and automatically adjusts home appliances based on ambient light and the location of people.
  • EducationStudents can take photos or upload images to allow the model to recognize and analyze charts, formulas, and other information in the textbook, obtaining detailed explanations and guidance, thus improving their learning efficiency.
  • HealthcareIn the medical field, it can quickly identify and analyze abnormal areas in medical images such as X-rays and CT scans, assisting doctors in making more efficient and accurate diagnoses.
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