graph LR
Application_Interfaces["Application Interfaces"]
Model_Loading_Configuration["Model Loading & Configuration"]
Core_Inference_Engine["Core Inference Engine"]
P_Tuning_Subsystem["P-Tuning Subsystem"]
P_Tuning_Web_Demo["P-Tuning Web Demo"]
Application_Interfaces -- "Requests Model Initialization" --> Model_Loading_Configuration
Model_Loading_Configuration -- "Provides Configured Model" --> Core_Inference_Engine
Application_Interfaces -- "Sends Prompt for Inference" --> Core_Inference_Engine
Core_Inference_Engine -- "Returns Generated Response" --> Application_Interfaces
P_Tuning_Subsystem -- "Loads Base Model for Fine-tuning" --> Model_Loading_Configuration
P_Tuning_Subsystem -- "Orchestrates Training & Evaluation" --> Core_Inference_Engine
P_Tuning_Web_Demo -- "Requests Fine-tuned Prediction" --> P_Tuning_Subsystem
P_Tuning_Subsystem -- "Provides Fine-tuned Response" --> P_Tuning_Web_Demo
click Application_Interfaces href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ChatGLM-6B/Application_Interfaces.md" "Details"
click Model_Loading_Configuration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ChatGLM-6B/Model_Loading_Configuration.md" "Details"
click Core_Inference_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ChatGLM-6B/Core_Inference_Engine.md" "Details"
click P_Tuning_Subsystem href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ChatGLM-6B/P_Tuning_Subsystem.md" "Details"
The ChatGLM-6B project is structured around a clear separation of concerns, facilitating both direct interaction and fine-tuning capabilities. At its core, the Core Inference Engine leverages the transformers.AutoModel to process prompts and generate responses. This engine is supported by a Model Loading & Configuration component, which efficiently loads and prepares the model and tokenizer for optimal performance. Users interact with the system through various Application Interfaces, including command-line, web, and API endpoints. For specialized tasks, the P-Tuning Subsystem provides a comprehensive framework for fine-tuning the model, complemented by a dedicated P-Tuning Web Demo for showcasing fine-tuned results. This modular design ensures a robust and scalable architecture for language model operations.
Application Interfaces [Expand]
The primary entry point for users and external applications to interact with the ChatGLM-6B model, consolidating command-line, web-based, and programmatic (API) interfaces.
Related Classes/Methods:
Model Loading & Configuration [Expand]
Responsible for loading the ChatGLM-6B model and its associated tokenizer into memory, configuring them for optimal performance across different hardware environments, and handling device mapping and optimization strategies.
Related Classes/Methods:
Core Inference Engine [Expand]
The central component embodying the ChatGLM-6B language model itself, responsible for the actual processing of input prompts and generating textual responses by leveraging the loaded model and tokenizer.
Related Classes/Methods:
P-Tuning Subsystem [Expand]
Manages the entire P-Tuning fine-tuning lifecycle, including data preparation, training loops, evaluation, prediction, and model saving/publishing to external hubs.
Related Classes/Methods:
A dedicated web interface specifically designed to demonstrate the capabilities and performance of the P-Tuning fine-tuned model, separate from the main model's web demo.
Related Classes/Methods: