

Wenwen Xinqun
Overview :
Wenwen Xinqun is an AI service platform targeted at enterprise-level users, focusing on providing solutions for the development and deployment of large model applications. It supports various models and chips, offering an end-to-end service experience, including model tuning, model services, development machines, tasks, and inference services. Wenwen Xinqun aims to help developers and enterprises quickly build and deploy AI applications, enhancing development efficiency and lowering technical barriers.
Target Users :
Wenwen Xinqun is designed for developers and enterprises that need rapid development and deployment of AI applications. By providing a one-stop AI development platform, it helps users save time on environment configuration, allowing them to focus on model optimization and application development, thereby accelerating time to market for products.
Use Cases
Enterprises rapidly deploy customer service chatbots using the Wenwen Xinqun platform.
Developers use the platform for fine-tuning and deploying image recognition models.
Educational institutions leverage platform resources for AI teaching and research.
Features
Model Plaza: Offers a variety of open-source and closed-source large models with on-demand access without deployment.
Experience Center: Intuitively compare the performance of multiple models and chips to experience model capabilities.
Model Tuning: One-click fine-tuning of models to better align with business needs.
Model Services: Deploy fine-tuned models on-demand without complex adaptations.
Development Machine: Online compilation, code debugging, and model development.
Tasks: Efficient and stable distributed training.
Inference Services: Elastic deployment of online model services.
Images: Easy management of operational environments.
Data Security: Store a rich variety of datasets.
Model Management: Simple management of multiple model versions.
How to Use
Visit the Wenwen Xinqun official website and register for an account.
Select the appropriate model and chip based on your requirements.
Choose or upload the needed model in the Model Plaza.
Use the Experience Center to test and compare model performance.
Optimize the model for specific business needs using model tuning features.
Configure model services and deploy them to the production environment.
Utilize the development machine for code writing and model debugging.
Create tasks for model training and optimization.
Deploy online models and provide API interfaces using inference services.
Maintain and update the operating environment through the image management tool.
Manage and back up datasets in the data security module.
Track and maintain different versions of models in model management.
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