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openPangu-VL-7B - Huawei's open-source multimodal model

openPangu-VL-7B is an open-source multimodal model launched by Huawei, optimized specifically for Ascend hardware. Combining language and vision capabilities, the model possesses powerful visual localization and OCR functions, enabling efficient processing of image, document, and video tasks.

What is openPangu-VL-7B?

openPangu-VL-7B is an open-source multimodal model launched by Huawei, optimized specifically for Ascend hardware. Combining language and vision capabilities, the model boasts powerful visual localization and OCR functions, efficiently processing image, document, and video tasks. It exhibits outstanding inference performance on Ascend chips, with a 720P image inference latency of only 160 milliseconds, making it suitable for edge deployment and individual development. openPangu-VL-7B's innovative visual encoder and training strategy enable it to excel in multimodal tasks, bringing new momentum to the Ascend ecosystem and helping developers explore more application scenarios.

Main functions of openPangu-VL-7B

  • Visual localization and target countingThe model can accurately identify and count target locations in an image, such as locating and counting all cherry tomatoes in a complex scene.
  • Document comprehension and OCRThe model can convert document screenshots into Markdown format, supports text recognition and chart understanding, and greatly improves document processing efficiency.
  • General Visual QuestionsIt supports understanding and answering questions related to image content, such as describing the scene in the image or explaining the details in the image.
  • Short Video UnderstandingThe model can analyze short video content, extract key information, and support rapid interpretation of video content.
  • Multimodal task processingIt supports a variety of multimodal tasks, such as visual reasoning and multi-graph understanding, and is suitable for a wide range of application scenarios.

Technical Principles of openPangu-VL-7B

  • Ascend native architectureThe model is specifically optimized for Ascend hardware and uses a visual encoder adapted for Ascend, which improves throughput by 15% compared to traditional encoders and significantly enhances inference performance.
  • Innovative visual encoderThe model combines 22 layers of window attention and 4 layers of full attention to enhance fine-grained visual understanding. Simultaneously, it employs a multi-label contrastive learning framework to solidify the foundation for visual localization tasks.
  • Hybrid Loss DesignBy using a hybrid approach of "weighted per-sample loss + per-token loss", the learning balance problem of samples of different lengths is solved, thereby improving the generalization ability of the model.
  • Positioning data format with paddingThe model uses a 000-999 thousandth percentile to fill relative coordinates, reducing the learning difficulty and improving the accuracy and efficiency of localization tasks.
  • Large-scale pre-trainingCompleted long-term stable training of a cluster without spikes using 3T+ tokens, providing developers with practical references for Ascend clusters and improving the model's versatility and adaptability.

Project address for openPangu-VL-7B

  • Project official websitehttps://ai.gitcode.com/ascend-tribe/openPangu-VL-7B
  • Technical Papers: https://ai.gitcode.com/ascend-tribe/openPangu-VL-7B/blob/main/doc/technical_report.pdf

Application scenarios of openPangu-VL-7B

  • Intelligent document processingThis tool quickly converts text and charts from document screenshots or scans into Markdown and other formats, improving document processing efficiency and saving time spent on manual transcription.
  • Visual Q&AAfter a user uploads an image, the model can answer questions related to the image content, such as describing the scene and recognizing objects. It is widely used in education and information retrieval.
  • Target localization and countingIt can be used to quickly locate and count parts in industrial production or manage shelf goods in retail settings, improving production efficiency and inventory management accuracy.
  • Short video content analysisThe model can automatically extract key information from short videos, generate video summaries or review content, and help users quickly understand videos and improve content management efficiency.
  • Intelligent Customer ServiceBy combining image and text information, we can provide users with more accurate customer service answers. For example, we can use product images to assist in consultations and improve the customer service experience.
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