Pretraining

As explained in Build a Large Language Model (From Scratch)

The initial training phase in which a language model learns general patterns from a large text corpus before task-specific adaptation.

Pretraining is the initial stage of LLM development: a model learns broad language patterns from a large, diverse corpus by predicting the next token. The resulting foundation model can then be fine-tuned for narrower tasks. section 1.3 section 1.8

GPT-like pretraining connects data preparation, a decoder-based transformer, attention, token prediction, and weight optimization. Large datasets expose the model to syntax, semantics, context, and general knowledge across many topics and languages. section 1.5 section 1.8

A practical pretraining workflow includes:

• Prepare tokenized input and target batches from unlabeled text. section 1.8 section 5.0
• Iterate over batches and epochs, reset gradients, calculate cross-entropy loss, backpropagate, and update weights with AdamW. section 5.2 section 5.6
• Evaluate training and validation losses, generate text samples, and monitor divergence as a signal of overfitting. section 5.2
• Save, resume, or load openly available weights when full-scale training is impractical. section 5.0 section 5.6

Small GPT-like training run

A PyTorch run initializes a GPTModel, moves it to a device, configures AdamW, and calls train_model_simple for ten epochs. With the prompt “Every effort moves you,” generated text changes from repeated punctuation and words to coherent, though sometimes memorized, passages. Training loss continues falling while validation loss stagnates, indicating overfitting.

Figure 1.3 Pretraining an LLM involves next-word prediction on large text datasets. A pretrained LLM can then be fine-tuned using a smaller labeled dataset.

Shows the two-stage flow from next-word prediction on large text datasets to fine-tuning on a smaller labeled dataset. section 1.3

How Build a Large Language Model (From Scratch) covers it

How deep it goes

Descends from definition to a PyTorch training loop, loss evaluation, sampling, and weight management, while deferring advanced optimization techniques.

Examples

Uses GPT-3’s corpus, a small educational dataset, PyTorch training code, loss plots, and generated continuations as concrete examples.

What it assumes

Assumes basic PyTorch and neural-network familiarity while teaching the training workflow, weight parameters, evaluation, and text monitoring.

What you can do after

Builds and evaluates a small GPT-like model, or loads pretrained weights for downstream fine-tuning.

How it connects

Connects next-token prediction with tokenization, transformer architecture, attention mechanism, fine-tuning, cross entropy loss, and AdamW optimizer.

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Also explained in

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  Deep Learning with R, Third EditionUnsupervised pretraining is training a model on input data without manually supplied labels before adapting it to downstream tasks.
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  Deep Learning with Python, Third EditionThe initial large-scale training phase used to learn broad language representations before task-specific fine-tuning.
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  Time Series Forecasting Using Foundation ModelsPretraining is initial training of a model on a large dataset to learn general patterns and relationships before applying it to specific tasks.
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  Inside Deep Learning: Math, Algorithms, ModelsA strategy of training a model on a source dataset before adapting it to a downstream task.
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  Build a Reasoning Model (From Scratch)The initial training stage in which a language model learns general statistical structure from large amounts of data before task-specific adaptation.
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  The Art of AI Product DevelopmentPretraining is the initial training of a model on broad data before adapting it to specific tasks or domains.
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  How Large Language Models WorkPretraining is the initial training of a model on broad data before adapting it to particular tasks or applications.
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  Introduction to Generative AI, Second EditionThe initial training phase in which a model learns general representations or predictive behavior from a large corpus before task-specific adaptation.
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  LLMs in ProductionPretraining is the initial training of a model on a large, broad dataset before adapting it to a specific task or domain.
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  Sutskever's ListPretraining is the initial training of a model on a broad dataset before task-specific adaptation or evaluation.
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  Data Without LabelsPretraining is an initial model-training stage that learns general patterns or representations before adaptation to a specific task.
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  Build a Website with ChatGPTPretraining is the initial training of a model on large amounts of data to learn general patterns before task-specific use.
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  Reinforcement Learning from Human FeedbackPretraining is the initial large-scale optimization of a model on broad data, commonly through next-token prediction.

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Related concepts

• foundational modelproduces · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
• pretrained modelproduces · A pretrained model is a machine-learning model whose parameters were learned through prior training before it is used or adapted for a subsequent task.
• next-token predictionuses · Next-token prediction is the task or training objective of estimating the token that follows a sequence, given its preceding tokens and any conditioning information.
• datasetuses · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
• zero-shot learningused by · Zero-shot learning is a setting in which a model performs a task or predicts categories without task-specific examples during training, adaptation, or inference, relying on learned knowledge and available task information.
• similarity searchused by · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.

This explainer was generated by Manning's knowledge graph from the text of Build a Large Language Model (From Scratch). The section links point to the parts of the book it draws on.