Large Model Training & Inference Platform

Manage data, model hub, deployment, and fine-tuning. Upload knowledge, model weights, and datasets with common LLM, embedding, and rerank models and custom deployment and fine-tuning services for diverse applications.

Manage data, model hub, deployment, and fine-tuning. Upload knowledge, model weights, and datasets with common LLM, embedding, and rerank models and custom deployment options.

Product Advantages

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Rich Model Resources

A marketplace for centrally displayed and shared models, providing users with an extensive model library and enabling users to upload models for custom deployments. These pre-trained models can be experienced and used on the model plaza.

Efficient Model Training Environment

Support users to use system preset models or custom models to train for specific tasks and data. The platform realizes parallel distributed training of multi-server and multi-GPU according to the conditions of the computing power platform, supports various acceleration frameworks such as DeepSpeed, and improves training efficiency.

Precisely Optimize Model Performance

The process of tuning and optimizing a specific task or dataset against an existing large model to improve the performance of the model on a specific application. Users can use the model fine-tuning function for customized training, and support common fine-tuning methods and parameter configurations such as LoRA and QLoRA.

Core Capabilities

Data Management

Comprehensive knowledge base file, model file, dataset management functions, support local upload and platform storage, use their own knowledge file combined with the natural language processing capabilities of the large model to create a professional intelligent knowledge assistant, you can also use the local large model file combined with computing power resources to create and deploy large model services.

Model Market

The model market is centrally displayed and shared, providing users with an extensive model library. Model types include LLM, Embedding, Rerark, and support users to upload custom deployed models to meet the needs of user diversified application scenarios.

Model Deployment

Flexible model deployment services are provided. Users can select all models in the model market for creation and deployment according to their needs, achieve rapid deployment and go-live, and support users to immediately experience and quickly use the deployed large model services, significantly improving deployment efficiency and reducing technical thresholds.

Model Trimming

Provide comprehensive fine-tuning task management, support through the precise fine-tuning function, users can upload local datasets according to the needs of specific application scenarios, and use common LoRA, QLoRA and other fine-tuning methods for customized parameter configuration and training, to help improve the accuracy and reliability in practical applications.

Core Capabilities

Comprehensive knowledge base file, model file, dataset management functions, support local upload and platform storage, use their own knowledge file combined with the natural language processing capabilities of the large model to create a professional intelligent knowledge assistant, you can also use the local large model file combined with computing power resources to create and deploy large model services.

Application Scenarios

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Intelligent Early Warning System for Financial Risks

Business Pain Points

Financial institutions face the challenges of large amounts of data, rapid changes, and complex situations. Traditional assessment relies on historical data and fixed algorithms, which make it difficult to capture market dynamics in real time and detect new risks in a timely manner. To build an intelligent system that can respond quickly, predict accurately, and provide personalized suggestions, it needs strong technical support.

Business Value

Efficient model deployment: With model deployment services, financial institutions can quickly integrate into existing systems by selecting a language or analytical model from the model market that suits their risk assessment needs, significantly shortening the deployment cycle.

Customized model fine-tuning: Financial institutions use this function to upload knowledge files and datasets according to their business characteristics, train the selected models accordingly, enhance their understanding of the industry, improve the accuracy of risk prediction, and ensure the good performance of the early warning system.