Fine-tuning

As explained in AI Agents in Action

A fine-tuned model is a pretrained model further trained on additional data for a narrower behavior or domain.

Fine-tuning further trains a pretrained language model so its data and behavior better match a particular use case or domain. A general foundation model can therefore become more specialized for chat, instruction, or a domain-specific agent. section 2.0

Use fine-tuning as a model-selection consideration when:

• A narrower domain matters more than broad general-purpose coverage. section 2.4
• A smaller specialized model may perform as well as or better than a larger general model. section 2.4
• The training method affects how well the model generalizes, reasons, or plans. section 2.4

ChatGPT as a progression

ChatGPT is trained on public internet data and then refined through several strategies, including reinforcement learning with human feedback, producing a chat-completions use case. section 2.0

Figure 2.2 The main elements that describe an LLM

Use the LLM-elements diagram to connect training data, model architecture, and fine-tuning to the resulting use case. section 2.0

How AI Agents in Action covers it

How deep it goes

Defines fine-tuning and relates it to staged training, but stops short of implementation steps or training configuration.

Examples

Uses ChatGPT’s staged training and the comparison between smaller fine-tuned models and larger general models.

What it assumes

Introduces generative models, LLM components, and training progression before distinguishing fine-tuning.

What you can do after

Evaluate whether a domain-specific or fine-tuned model suits an agent better than a larger general-purpose model.

How it connects

Connects fine-tuning with LLMs, GPTs, chat completions, reinforcement learning with human feedback, and domain-specific agents.

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Also explained in

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  LLMs in ProductionFine-tuning is the process of continuing model training on task-specific data to adapt a pretrained model to a particular behavior or domain.
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  Natural Language Processing in Action, Second EditionFine-tuning is the continued training of a pretrained model on a narrower dataset or task so its parameters adapt to that target domain or behavior.
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  Generative AI in ActionTask-specific fine-tuning is the adaptation of a pretrained model using data targeted at a particular task.
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  Software Testing with Generative AIFine-tuning is the process of adapting a pretrained model to a task or domain by training it further on targeted data.
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  The Art of AI Product DevelopmentFine-tuning is adapting a pretrained model by training it further on task- or domain-specific data.
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  Reinforcement Learning from Human FeedbackFine-tuning is further training of a pretrained model on a task-specific or behavior-specific objective.
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  Deep Learning with R, Third EditionFine-tuning is the continued training of a pretrained model, typically with a small learning rate, to adapt its learned representations to a new task.
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  Deep Learning with Python, Third EditionAdapting a pretrained model to a new task by continuing training, usually with a smaller learning rate or on additional data.
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  Build a Large Language Model (From Scratch)Further training of a pretrained model on a smaller task-specific dataset to improve downstream performance.
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  AI-Powered SearchFine-tuning is additional training of a pretrained model on task-specific data to adapt its behavior or performance.
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  Grokking AI ApplicationsFine-tuning is additional training of a pretrained model on more specific data or objectives to adapt its behavior.
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  How Large Language Models WorkFine-tuning is an additional training stage that adapts a pretrained model to a particular task or behavior using a smaller, task-relevant dataset.
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  Time Series Forecasting Using Foundation ModelsFine-tuning is additional training of a pretrained model on task- or domain-specific data by updating some or all of its parameters.
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  Deep Learning with PyTorch, Second EditionThe process of adapting a pretrained model to a new task by continuing training on task-specific data.
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  Introduction to Generative AI, Second EditionFine-tuning is adapting a pretrained model through continued training for a particular task or behavior.
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  Data Analysis with LLMsFine-tuning is the process of further training an existing model on task-specific examples to adapt its behavior to a particular task.
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  Build a Text-to-Image Generator (from Scratch)Fine-tuning is adapting a pretrained model to a new task by updating selected parameters, often only the task-specific output layers.
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  Domain-Specific Small Language ModelsTraining a pretrained model further on task- or domain-specific data to adapt it to a narrower use case.
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  Building Reliable AI SystemsFine-tuning is the adaptation of a pretrained model by further training it, typically on task- or domain-specific data.
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  Data Storytelling with Altair and AIFine-tuning is the additional training of a pretrained machine-learning model on targeted data to adapt its parameters to a task or domain.
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  Starting Data Analytics with Generative AI and PythonFine-tuning is the further training of a pretrained model on targeted data to adapt its behavior or performance.
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  Transformers in ActionFine-tuning is the adaptation of a pretrained model by further optimizing its parameters for a specific task or behavior.
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  Hugging Face in ActionFine-tuning is the adaptation of a pretrained model by continuing training on data for a particular task or domain.
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  The AI Pocket BookAdditional training applied to an existing model to adapt or improve its behavior using new examples.
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  Microsoft Azure in ActionFine-tuning is the process of further training a pretrained model on task-specific data to adapt its behavior or vocabulary.
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  Build Applications with Local AI Models on a MacFine-tuning is the process of further training a pretrained model on selected examples to adapt its behavior or capabilities.
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  AI Agents and ApplicationsFine-tuning is the additional training of a pretrained model on a narrower or task-specific dataset to adapt its behavior.
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  Machine Learning System DesignThe process of continuing training on an existing model to adapt its parameters to new data or a related task.
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  The Complete Obsolete Guide to Generative AIFine-tuning is continued training of a pretrained model on task- or domain-specific data to specialize its behavior.
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  Causal AIFine-tuning is the process of adapting a pretrained model by continuing parameter training on task-specific or domain-specific data.
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  Knowledge Graphs and LLMs in ActionFine-tuning is adapting a pretrained model by continuing its training on targeted data or objectives.
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  Machine Learning Algorithms in DepthFine-tuning is adapting a pretrained model to a target task by continuing training, typically with task-specific output parameters.
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  Data Without LabelsFine-tuning is the adaptation of a pretrained model's parameters to optimize performance on a target task.
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  Machine Learning Platform EngineeringFine-tuning is the additional training of a pretrained model on task-specific data to adapt its behavior to a new task or domain.
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  Outlier Detection in PythonFine-tuning is the continued training of a pretrained model, usually on a new task or domain, by adjusting some or all of its parameters.
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  Financial AI in PracticeFine-tuning adapts a pretrained model by continuing training on task-specific or domain-specific data.

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Related concepts

• retrieval-augmented generationcontrasts with · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
• prompt engineeringcontrasts with · Prompt engineering is the deliberate design and refinement of instructions, context, examples, or other inputs to guide an AI model’s behavior or outputs.
• custom modelproduces · A custom model is a machine-learning model trained, adapted, or configured for a particular domain, task, or set of requirements.
• reinforcement learningcontrasts with · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
• regressionproduces · A regression is an unintended deterioration in a system’s behavior, performance, or capabilities relative to a prior or expected level, often following a change.

This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action. The section links point to the parts of the book it draws on.