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Shusheng·Wanxiang InternVL 2.5 - Shanghai AI Lab's open-source multimodal large language model series

Shusheng·Wanxiang InternVL 2.5 is an open-source series of large-scale multimodal language models (MLLMs) developed by the OpenGVLab team at the Shanghai AI Lab. This series of models significantly enhances upon InternVL 2.0, particularly in...

What is the Scholar's Myriad Phenomena InternVL 2.5?

Shusheng·Wanxiang InternVL 2.5 is an open-source series of large-scale multimodal language models (MLLMs) launched by the OpenGVLab team at the Shanghai AI Lab. This series significantly enhances upon InternVL 2.0, particularly in training and testing strategies and data quality. InternVL 2.5 includes models ranging from 1B to 78B in size, adapting to different use cases and hardware requirements. InternVL2_5-78B is the first open-source model to score over 70 on the Multimodal Understanding (MMMU) benchmark, surpassing commercial models such as ChatGPT-4o and Claude-3.5-Sonnet. InternVL 2.5 achieves performance improvements based on Chained Thinking (CoT) inference technology, demonstrating powerful multimodal capabilities in multiple benchmark tests, including multidisciplinary inference, document understanding, and multi-image/video understanding.

The main functions of Scholar's World in InnVL 2.5

  • Multimodal understandingProcessing and understanding information from different modalities (text, images, video).
  • Multidisciplinary reasoning: To perform complex reasoning and problem-solving across multiple disciplines.
  • Real-world understandingTo understand and analyze real-world scenarios and events.
  • Multimodal hallucination detection: Identify and distinguish between real and fictional visual information.
  • Visual groundingMatch text descriptions with actual objects in images.
  • Multilingual processingIt supports the understanding and generation capabilities of multiple languages.
  • Pure Language ProcessingPerform language tasks such as text analysis, generation, and understanding.

The technical principles of Scholar's World of Things InternVL 2.5

  • ViT-MLP-LLM architectureCombining Visual Transformer (ViT) and Large Language Model (LLM) based MLP projector.
  • Dynamic high-resolution trainingIt adapts to inputs of different resolutions and optimizes the processing of multiple image and video data.
  • Pixel inversion operationReduce the number of visual tokens and improve model efficiency.
  • Progressive scaling strategyStart training with a small-scale LLM and gradually expand to larger-scale models.
  • Random JPEG compressionSimulates internet image degradation to enhance the model's robustness to noisy images.
  • Loss reweightingTo balance the NTP loss of responses of different lengths and optimize model training.

The project address for Shusheng·Wanxiang InnVL 2.5

Application Scenarios of Scholar's Universal InternVL 2.5

  • Image and video analysisIt is used for automatic annotation, classification and understanding of image and video content, and is applicable to fields such as security monitoring, content moderation, and media entertainment.
  • Visual Question Answering (VQA)In fields such as education, e-commerce, and customer service, it answers questions related to image or video content, providing a richer user experience.
  • Document comprehension and information retrievalExtract key information from large amounts of documents in fields such as law, medicine, and academic research to support complex queries and research.
  • Multilingual translation and comprehensionInternVL 2.5 supports multilingual processing, playing a role in cross-language communication, international business, and global content creation.
  • Assisting with design and creative workIn the design and creative industries, I help understand and realize complex visual ideas, such as architectural design and advertising concepts.
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