🪐Model Network Layer

Model Network Layer

Multi-model Compatibility

The Model Network Layer supports various types including deep learning models, traditional machine learning models, and pre-trained models, with a primary focus on Large Language Models (LLM). While handling large-scale model parameters and data volumes, this layer can also seamlessly integrate models from multiple fields such as natural language processing and computer vision.

Modular Design

The system adopts a modular architecture, encapsulating each model as an independent module, similar to GitHub's repository management approach. This design facilitates individual updates and maintenance of models, while tracking change history through version control systems, ensuring project traceability and stability.

Unified Interface

The system provides a unified API interface to simplify model calling processes. Developers can complete model loading, inference, and evaluation through this standardized interface without needing to understand underlying implementation details. This not only improves development efficiency but also makes the integration of various model platforms more convenient.

Integration and Deployment

The system can seamlessly integrate with existing development toolchains and deployment platforms. Following Hugging Face's model library design, the Model Network Layer provides convenient model import and export functionality, supporting various deployment environments including local servers, cloud platforms, and edge devices.