Convenient Big Model - A multimodal AI model launched by CloudWalk Technology

What is the Rongda model?

Congrong Big Model is a multimodal AI model launched by CloudWalk Technology. The model topped the OpenCompass multimodal benchmark with a score of 80.7, surpassing leading teams such as Google and OpenAI. Focusing on general visual language understanding and reasoning tasks, the model builds a globally leading technological barrier based on core technological breakthroughs such as multimodal alignment, human-like decision-making, efficient engineering optimization, and native multimodal reasoning. Congrong Big Model has demonstrated outstanding performance in multiple fields including medical health, mathematical logic, and art design, and has achieved large-scale deployment in finance, manufacturing, and government sectors, contributing to intelligent transformation.

The main functions of the large-scale model

• Visual perception and cognitive understandingIt supports processing visual information (such as images and videos) for cognitive understanding, and performs particularly well in fields such as medical health and art design, and can understand complex visual scenes.
• Cross-domain applicationsDemonstrates strong comprehension and reasoning abilities in multiple professional fields (such as mathematical logic, medical health, art and design, etc.).
• Text recognition in complex scenesIt enables text recognition in complex scenarios (such as OCRbench), supports processing high-resolution images and documents (such as contracts, invoices, etc.), and supports tasks such as intelligent review, intelligent parsing, and intelligent question answering.
• Open Domain QuestionsIt performs exceptionally well in open-domain question answering (such as MMVet), providing accurate and in-depth answers.

Technical principles of Rongda model

• Multimodal alignmentWe construct high-quality benchmark datasets covering various task scenarios and enhance the model's understanding and reasoning capabilities for multimodal data based on reinforcement instruction alignment. We integrate DPO and GRPO techniques to optimize the model's learning mechanism, enabling the model to make decisions and reason in a way that more closely resembles human thinking, achieving human-like reasoning without relying on reward models.
• High-efficiency engineering optimizationFor high-resolution image and multimodal document understanding tasks, the image encoder of the model is structurally optimized to efficiently process high-resolution images and complex documents. The model's context modeling capability is optimized to accurately track logical relationships in long texts, supporting tasks such as cross-page document analysis and multi-turn dialogue.
• Native multimodal reasoningUpgrade the model architecture to handle multi-graph and cross-graph scenarios with interlaced text and graph modes and native video modes, enabling complex multimodal tasks such as cross-graph comparison, text-graph combination reasoning, and multi-graph question answering.

Application scenarios of Rongda model

• Financial risk controlCollaborate with banks to build an AI-powered risk control system to automate risk identification and reduce the number of complaints.
• Intelligent Customer ServiceDeploy intelligent customer service platforms for e-commerce platforms to improve the accuracy of Q&A and customer service efficiency.
• Medical HealthIt processes medical images to assist doctors in diagnosis and improve diagnostic accuracy and efficiency.
• Government AffairsProcess government documents to achieve intelligent review and Q&A, and optimize public services.
• manufacturingUsed in product quality testing to improve production efficiency and product quality.