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

Author: AI小集 · Original source: AI工具集

What is openPangu-VL-7B

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

openPangu-VL-7B

Main features of openPangu-VL-7B

  • Visual grounding and object counting: The model can accurately identify the positions of targets in images for counting, for example, locating and counting all cherry tomatoes in a complex scene.
  • Document understanding and OCR: The model can convert document screenshots into Markdown format, support text recognition and chart understanding, and greatly improve document processing efficiency.
  • General visual question answering: Supports understanding and answering questions related to image content, such as describing the scene in a picture or explaining details in an image.
  • Short video understanding: The model can analyze short video content, extract key information, and support rapid interpretation of video content.
  • Multimodal task processing: Supports a variety of multimodal tasks, such as visual reasoning and multi-image understanding, adapting to a wide range of application scenarios.

Technical principles of openPangu-VL-7B

  • Ascend-native architecture: The model is specifically optimized for Ascend hardware and uses a visual encoder adapted to Ascend, increasing throughput by 15% compared with traditional encoders and significantly enhancing inference performance.
  • Innovative visual encoder: Combines 22 layers of window attention and 4 layers of full attention to improve fine-grained visual understanding. At the same time, the model adopts a multi-label contrastive learning framework, laying a solid foundation for visual grounding tasks.
  • Hybrid loss design: Through a hybrid scheme of "weighted per-sample loss + per-token loss," it solves the problem of balanced learning across samples of different lengths and improves the model's generalization ability.
  • Padded localization data format: The model uses 000-999 thousandths padded relative coordinates, reducing learning difficulty and improving the accuracy and efficiency of localization tasks.
  • Large-scale pretraining: Completed 3T+ tokens of spike-free long-stable cluster training, providing developers with practical reference for Ascend clusters and improving the model's generality and adaptability.

Project address of openPangu-VL-7B

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

Application scenarios of openPangu-VL-7B

  • Intelligent document processing: Quickly converts text and charts in document screenshots or scans into formats such as Markdown, improving document processing efficiency and saving manual transcription time.
  • Visual question answering: After users upload images, the model can answer questions related to the image content, such as describing the scene and recognizing objects, and is widely used in education and information queries.
  • Object localization and counting: Quickly locate and count parts in industrial production, or manage shelf products in retail scenarios, improving production efficiency and inventory management accuracy.
  • Short video content analysis: The model can automatically extract key information from short videos, generate video summaries or review content, helping users quickly understand videos and improve content management efficiency.
  • Intelligent customer service: Combining image and text information to provide users with more accurate customer service answers, for example, assisting consultation through product images, enhancing the customer service experience.