Build an LLM from Scratch 6: Finetuning for Classification

In this coding along video, Sebastian Raschka discusses fine-tuning a pre-trained GPT model for practical applications, specifically email spam classification. He explains the process of preparing the dataset, setting up PyTorch data loaders, and modifying the model architecture for classification tasks, emphasizing the replacement of the output layer. He also touches on the importance of calculating classification loss and accuracy, and shares bonus materials, including additional experiments, application to a movie review dataset, and a simple user interface. The goal is to adapt the LLM for binary classification, predicting whether an email is spam or not, and sets the stage for instruction fine-tuning in the subsequent chapter.

Outlines

Part 1: Introduction and Data Preparation

Part 2: Data Loaders and Model Setup

Part 3: Fine-Tuning and Evaluation

Part 4: Application and Conclusion

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Sebastian Raschka

Part 1: Introduction and Data Preparation

Introduction to Fine-Tuning LLMs for Classification

Dataset Preparation for Spam Classification

Dataset Balancing and Splitting

Part 2: Data Loaders and Model Setup

Creating PyTorch Data Loaders and Addressing Sequence Lengths

Initializing the Dataset and Data Loaders

Loading Pre-trained Weights and Initial Model Evaluation

Modifying the GPT Model for Classification Fine-Tuning

Part 3: Fine-Tuning and Evaluation

Implementing Evaluation Utilities: Loss and Accuracy Calculation

Implementing Loss Calculation and Preparing for Fine-Tuning

Fine-Tuning the Model on Supervised Data

Bonus Material: Additional Experiments on Fine-Tuning

Part 4: Application and Conclusion

Using the LLM as a Spam Classifier and Concluding Remarks

Build an LLM from Scratch 6: Finetuning for Classification

Sebastian Raschka

Part 1: Introduction and Data Preparation

00:01Introduction to Fine-Tuning LLMs for Classification

Introduction to Fine-Tuning LLMs for Classification

05:38Dataset Preparation for Spam Classification

Dataset Preparation for Spam Classification

16:05Dataset Balancing and Splitting

Dataset Balancing and Splitting

Part 2: Data Loaders and Model Setup

27:04Creating PyTorch Data Loaders and Addressing Sequence Lengths

Creating PyTorch Data Loaders and Addressing Sequence Lengths

37:01Initializing the Dataset and Data Loaders

Initializing the Dataset and Data Loaders

42:54Loading Pre-trained Weights and Initial Model Evaluation

Loading Pre-trained Weights and Initial Model Evaluation

52:59Modifying the GPT Model for Classification Fine-Tuning

Modifying the GPT Model for Classification Fine-Tuning

Part 3: Fine-Tuning and Evaluation

1:08:17Implementing Evaluation Utilities: Loss and Accuracy Calculation

Implementing Evaluation Utilities: Loss and Accuracy Calculation

1:16:17Implementing Loss Calculation and Preparing for Fine-Tuning

Implementing Loss Calculation and Preparing for Fine-Tuning

1:30:59Fine-Tuning the Model on Supervised Data

Fine-Tuning the Model on Supervised Data

1:47:47Bonus Material: Additional Experiments on Fine-Tuning

Bonus Material: Additional Experiments on Fine-Tuning

Part 4: Application and Conclusion

2:04:30Using the LLM as a Spam Classifier and Concluding Remarks

Using the LLM as a Spam Classifier and Concluding Remarks