B2.6 Fine-tune GPT-4o for WordPress shortcode generation using final version of training data

[BillyHeYifan]

• [x] Prepare the dataset for fine-tuning by ensuring it is cleaned and formatted in JSONL format.
• [x] Use the integrated dataset for training GPT-4O, ensuring all configurations are correctly set for the fine-tuning process.
• [x] Set up the necessary parameters such as learning rate, batch size, and evaluation metrics for optimal performance.
• [x] Monitor the fine-tuning process to track model performance and make adjustments as needed.
• [x] Validate the fine-tuned model using the validation set, and compare its performance with baseline models.
• [x] Document the results and any necessary changes made during the fine-tuning process for future reference.

[BillyHeYifan]

Training Data Log

[Guanweiyi1999]

Looks good for me~ In this task, the user fine-tuned the GPT-4O model for WordPress shortcode generation and completed a series of important steps. First, the user ensured the cleanliness and correct format of the training dataset, especially the preparation of the JSONL format, which provided a reliable foundation for subsequent fine-tuning. Second, during the training process, the user correctly configured the parameters required for fine-tuning, such as learning rate, batch size, and evaluation metrics, to ensure the effectiveness of fine-tuning. By monitoring the performance of the model, the user can flexibly adjust the parameters to improve the performance of the model. Finally, after evaluation on the validation set, the user compared the performance of the fine-tuned model and compared it with the baseline model to ensure the improvement of the model in shortcode generation. The detailed documentation of the entire process also provides a valuable reference for future projects.