The tutorial provides a comprehensive guide on fine-tuning the GPT-Neo model from Aleuther AI for a text classification task, using a student questions dataset of approximately 120,000 entries narrowed down to 5,000 for efficiency. The dataset, formatted as a CSV file with 'text' and 'label' columns, undergoes preprocessing via a Python script that partitions it into training and testing sets, reserving 80% for training. Fine-tuning involves configuring an application in the Clarifai Community, uploading the training data, and selecting the GPT-Neo model template for training. The fine-tuning is conducted on two versions of the model: a 125 million parameter version and a 2.7 billion parameter version, both of which are subsequently evaluated. The 125 million parameter model achieves an AUC of 92.86, while the 2.7 billion parameter model reaches 99.07, though both metrics were initially calculated with the training dataset. Testing with unseen data shows slight performance degradation, yet the 2.7 billion parameter model maintains robust performance, demonstrating the model's effective adaptation for text classification tasks.