Pretraining

As explained in LLMs in Production

Pretraining is the initial training of a model on a large, broad dataset before adapting it to a specific task or domain.

Pretraining gives a model a broad starting point before task-specific adaptation. Training on a large corpus such as Wikipedia, books, or the internet helps an LLM learn language patterns, syntax, semantics, and world knowledge. section 5.2.2

The trade-off is substantial cost: pretraining requires many iterations and significant computation, while later finetuning usually uses much less data and fewer resources. Reusing a pretrained model is therefore often more practical than training from scratch. section 5.2.2

Pretraining establishes the foundation for later model work:

• Learns broad language patterns from a large, diverse corpus. section 5.2.2
• Provides a reusable model for tasks such as sentiment analysis, text classification, and question answering. section 5.2.2
• Supports an end-to-end workflow that can continue through quantization, LoRA finetuning, and production deployment. section 9.0

From broad training to a specialized model

A model can first learn from a general corpus, then adapt to a specific domain or task. A practical project follows this pattern by pretraining a model from scratch, making production-oriented improvements, applying LoRA, and serving it at an endpoint. section 5.2.2 section 9.0

Figure 5.3 Finetuning differs from training from scratch in that you don’t have to define model behavior, you can use the exact same training loop, and you have a fraction of the data requirement.

Compare pretraining with finetuning through data requirements, model-behavior definition, and reuse of the training loop. section 5.2.2

How LLMs in Production covers it

How deep it goes

Explains corpus purpose, learned capabilities, cost, and transfer workflow, but stops short of detailed research internals.

Examples

Uses Wikipedia, books, the internet, and a from-scratch Llama 3 project as training contexts.

What you can do after

Build it from scratch as part of an end-to-end LLM project, then prepare, adapt, and deploy the model.

How it connects

Connects pretraining with transfer learning, finetuning, LoRA, quantization, and deployment.

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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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  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.
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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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  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 LLMs in Production. The section links point to the parts of the book it draws on.