Build an LLM from Scratch 6: Finetuning for Classification

2025-04-04 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, extended

Summary

This content introduces fine-tuning a pre-trained GPT model for classification tasks, specifically email spam detection. It details the process of preparing a small SMS spam collection dataset from UCI, which includes downloading, inspecting, balancing, and converting labels to integers. The data is then split into training, validation, and test sets (70%, 10%, 20% respectively). The tutorial explains setting up PyTorch data loaders, emphasizing sequence padding to a uniform length (120 tokens) using the end-of-text token (50256). The pre-trained GPT model, specifically the 124 million parameter version, is loaded, and its output layer is replaced with a smaller, two-node layer for binary classification (ham/spam). The majority of the model's parameters are frozen, with only the new output head, the final layer normalization, and the last Transformer block made trainable to prevent overfitting and reduce training time. The content also covers implementing evaluation utilities for calculating classification loss and accuracy, demonstrating initial model performance around 50% accuracy, and then proceeds to fine-tune the model over five epochs, achieving 97% training and validation accuracy, and 95% test accuracy in approximately 11 minutes on a MacBook Air.

Key takeaway

For AI Engineers and ML practitioners building classification systems, fine-tuning a pre-trained LLM by replacing its output layer and selectively freezing parameters offers a highly efficient path to strong performance. Your focus should be on preparing a balanced dataset and strategically unfreezing only the necessary layers to adapt the model without extensive retraining, ensuring faster development and reduced computational cost. Consider using a small, simple dataset first to establish a baseline and validate your fine-tuning approach.

Key insights

Fine-tuning pre-trained LLMs for specific classification tasks is efficient and effective, even with smaller models.

Principles

• Balance datasets to simplify evaluation metrics.
• Freeze most pre-trained layers to prevent overfitting.
• Pad sequences to a consistent length for batch processing.

Method

Prepare a balanced, labeled dataset, replace the LLM's output layer for classification, freeze most pre-trained weights, and fine-tune only the new output layer and final Transformer block.

In practice

• Use `torch.no_grad()` for inference to save computation.
• Apply `torch.argmax()` directly to logits for predicted labels.
• Save fine-tuned models using `model.state_dict()` for later use.

Topics

• LLM Fine-tuning
• Text Classification
• GPT Architecture
• Data Balancing
• Output Layer Modification

Best for: Machine Learning Engineer, AI Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sebastian Raschka.