Foundational model

As explained in How Large Language Models Work

A foundation model is a broadly trained model that can be adapted or used as a basis for many applications.

A foundational model is a pretrained model that provides a starting point for building task-specific systems. It can support many applications, especially when made available for others to use and adapt. section 1.6 section 5.0

The model is not the finished application. Pretraining produces the base; fine-tuning then adapts it with a smaller dataset, while additional code can constrain outputs afterward. The treatment stays general rather than showing how to code an LLM from scratch. section 1.6 section 5.0

Figure 5.1 One may intervene to change or constrain an LLM’s behavior in four places. The two stages of model training are shown in the middle of the diagram, where the model’s parameters are altered. On the left, one could also alter the training data before model training. On the right, one could intercept the model outputs after model training and write code to handle specific situations.

Use the intervention diagram to trace four ways of shaping behavior: changing training data, pretraining, fine-tuning, and handling outputs with code. section 5.0

How How Large Language Models Work covers it

How deep it goes

Defines the pretraining-to-fine-tuning relationship but stops short of implementing an LLM from scratch.

Examples

Uses ChatGPT, Claude, Gemini, Llama, Copilot, EleutherAI models, and BigScience models as examples of the surrounding LLM landscape.

What you can do after

Apply it by fine-tuning a pretrained model into a task-specific system.

How it connects

Connects pretraining, fine-tuning, task-specific models, LLMs, and output handling.

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Related concepts

• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• pretrained modelkind of · 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.
• feature engineeringused by · Feature engineering is the creation, transformation, or selection of input features from data to provide useful representations for analysis or modeling.
• TimesFMincludes · TimesFM is a foundation model designed for deterministic time-series forecasting.
• instruct modelincludes · An instruction-tuned model is a pretrained model further trained to follow natural-language instructions and produce task-appropriate responses.

This explainer was generated by Manning's knowledge graph from the text of How Large Language Models Work. The section links point to the parts of the book it draws on.