Update train.py - Githubissues

Refactor and Optimize Model Training Script

This commit introduces significant improvements and optimizations to the model training script used in the Yugabyte database project. The training script has been enhanced for better code organization, readability, and configurability, providing users with a more intuitive experience.

Changes Made:

Argument Parsing Enhancement: The script now employs the argparse module to parse command-line arguments effectively. This change facilitates easy customization of the model, data, and training hyperparameters, allowing users to fine-tune the training process to suit their specific needs.

CustomTrainer Class: A new class named CustomTrainer has been added, extending the Trainer class from the Transformers library. This custom trainer class provides improved data loading mechanisms by implementing get_train_dataloader and get_eval_dataloader methods with distributed data loading support.

Model Configuration Flexibility: The script now offers more flexibility in configuring the model based on specific arguments. Users can choose to use LORA (Language Oriented Reliability Architecture) with various hyperparameters or opt for a standard model without LORA. The script automatically adjusts the model architecture and gradient checkpointing based on user preferences.

Code Comments and Clarity: Comprehensive and informative comments have been added throughout the script to guide users on where to make specific model configurations based on command-line arguments. These comments improve code readability and aid in understanding the underlying logic.

Code Optimization: Redundant and unnecessary statements have been removed, resulting in cleaner code and improved execution efficiency.

The enhancements introduced in this commit ensure that the model training script adheres to best practices and is well-optimized for both beginners and experienced users. By leveraging the power of the Transformers library and customizing the training process with ease, users can achieve more efficient and accurate model training for their specific tasks

X-PLUG / mPLUG-Owl

Update train.py #111