Improve Model Training Explainability and Debugging Features

[vamoko]

While training models with LLM-And-More, I've observed that enhancing the explainability and debugging capabilities during the training process could significantly improve user experience and development efficiency. Here are my specific suggestions:

1. Implement Explainability Tools for Training: Understanding the learning process and decision-making basis of the model is crucial for developers. It's recommended to add visualization tools to LLM-And-More that display key metrics, changes in loss functions, gradient information, etc., during training. This would help developers intuitively comprehend the model's training status and performance.

2. Support Debugging Features: Developing and debugging models often require experimenting with different hyperparameters, data preprocessing methods, etc., to optimize model performance. It's suggested to introduce debugging features in LLM-And-More, such as an interactive debugging interface that allows users to observe the model's predictions on small sample data in real-time, facilitating prompt identification and resolution of issues.

3. Provide Model Explainability Tools: For large-scale models, understanding the decision-making process and the explainability of predictions is crucial. It's recommended to incorporate explainability tools into LLM-And-More, such as visualizing feature importance, generating explanatory texts, etc., to help users understand the principles and logic behind model predictions.

These improvements would aid in enhancing users' understanding and control over the model training process, accelerating the debugging and optimization stages. I hope the LLM-And-More team will consider and implement these features to further increase the project's practicality and user-friendliness. Thank you for your attention to developers' needs!