Design a Large Language Model Fine-Tuning System

Functional Requirements:

• Model Selection: Ability to select and load pre-trained language models for fine-tuning.
• Data Ingestion: Support for ingesting diverse datasets for training, including text, CSV, JSON, etc.
• Preprocessing: Tools for data cleaning, tokenization, and augmentation.
• Training Configuration: Options to configure hyperparameters such as learning rate, batch size, and epochs.
• Fine-Tuning: Mechanism to fine-tune the model on the provided dataset.
• Evaluation: Evaluate model performance using metrics like accuracy, F1-score, and loss.
• Version Control: Track different versions of the fine-tuned models.
• Deployment: Deploy the fine-tuned model as an API for inference.
• Monitoring: Monitor model performance and resource usage in real-time.
• User Interface: Provide a user-friendly interface for managing the fine-tuning process.

Non-Functional Requirements:

• Scalability: System should handle increasing data sizes and concurrent fine-tuning tasks.
• Performance: Ensure low latency in model inference and efficient resource utilization during training.
• Reliability: System should be robust against failures and ensure data integrity.
• Security: Implement data encryption, access control, and secure API endpoints.
• Usability: Intuitive interface and comprehensive documentation for users.
• Maintainability: Codebase should be modular and easy to update or extend.
• Compliance: Adhere to data privacy regulations like GDPR.
• Cost Efficiency: Optimize resource usage to minimize operational costs.
• Interoperability: Support integration with existing ML pipelines and tools.