Build an LLM from Scratch 5: Pretraining on Unlabeled Data

2025-03-23 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

This chapter details the pre-training of large language models, specifically a GPT-like architecture, by integrating data loading, multi-head attention, and the GPT model architecture. It covers the implementation of text generation, evaluation of generative models using cross-entropy loss and perplexity, and the calculation of training and validation set losses. The content then progresses to the core LLM training pipeline, including gradient computation and weight updates, and introduces advanced text generation strategies like temperature scaling and Top-K sampling. Finally, it demonstrates how to save and load custom models and, crucially, how to load pre-trained GPT-2 weights from OpenAI into the custom architecture, showcasing significantly improved text generation capabilities compared to a minimally trained model.

Key takeaway

For AI Engineers and ML practitioners building or fine-tuning generative models, understanding the full pre-training pipeline, from data preparation and loss calculation to advanced decoding strategies, is crucial. You should prioritize robust loss functions like cross-entropy for optimization and experiment with temperature scaling and Top-K sampling to balance creativity and coherence in generated text. Be aware that small datasets lead to overfitting, necessitating pre-trained weights or larger datasets for practical applications.

Key insights

Pre-training LLMs involves integrating architectural components, optimizing text generation via loss functions, and employing advanced sampling techniques.

Principles

• Cross-entropy loss quantifies text generation quality.
• Temperature and Top-K sampling control text generation randomness.
• Overfitting is common with small datasets and extended training.

Method

The training pipeline iterates over epochs and batches, computes cross-entropy loss, backpropagates gradients, and updates model weights using an optimizer like AdamW, optionally evaluating progress and generating sample text.

In practice

• Use `torch.nn.functional.cross_entropy` for efficient loss calculation.
• Implement `model.eval()` to disable Dropout during inference.
• Save and load model `state_dict` for persistence.

Topics

• Large Language Model Pre-training
• GPT Model Architecture
• Cross-Entropy Loss
• Text Generation Decoding
• PyTorch Model Loading

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.