Fine-tuning GPT for Text Classification: Spam Detection & Pretrained Weights | Chapter 6

About this lesson

Download the source code from here: https://onepagecode.substack.com/ In Chapter 6 of "Build a Large Language Model (From Scratch)", we take the pretrained GPT model from the previous chapter and fine-tune it for a specific classification task: detecting spam messages. We cover the full process of classification fine-tuning, including preparing a labeled dataset, modifying the model architecture by adding a classification head, calculating classification loss and accuracy, and implementing an efficient training loop. By the end of this chapter, you will have a fine-tuned GPT model that can accurately classify text messages as “spam” or “not spam.” What you’ll learn in this chapter: • The difference between instruction fine-tuning and classification fine-tuning • Preparing and balancing a text classification dataset • Creating PyTorch data loaders for classification tasks • Loading pretrained GPT-2 weights into our custom model • Adding and training a classification output layer • Calculating classification loss and accuracy • Fine-tuning the model on labeled data • Using the fine-tuned model to classify new text messages This chapter bridges the gap between pretraining and practical applications of LLMs. #LLMFineTuning #ClassificationFineTuning #SpamDetection #GPT2FineTuning #BuildLLMFromScratch #Chapter6

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