Glossary

Parameter-efficient fine-tuning (PEFT)

What is Parameter-efficient fine-tuning (PEFT)?

Parameter-efficient fine-tuning (PEFT) covers techniques that adapt a large pre-trained model to a task by training only a small portion of its parameters or adding a few extra ones. Well-known methods include LoRA, QLoRA, adapters and prefix tuning. This greatly reduces computing and memory requirements, and small add-on modules can be stored for several tasks instead of full model copies. Example: a language model is adapted to the style of internal technical documents.

Topics

fine-tuning
training
efficiency

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Related terms

Fine-tuning

Fine-tuning means further training an already pre-trained AI model on additional, usually task-specific data. Its internal weights are adjusted so that the model handles, for instance, a particular writing style, specialist vocabulary or output format more reliably. Fine-tuning requires carefully prepared example data, computing power and testing. For many applications, well-written instructions and connected knowledge are sufficient and easier to change.

Model training
Customisation
+1

Instruction Tuning

Instruction tuning is a form of fine-tuning in which a pre-trained language model is trained further on many pairs of instructions and desired responses. This teaches the model to follow tasks phrased in natural language rather than merely continue text. It is a key step in turning a base model into an assistant and is often combined with methods such as RLHF. Example: 'Summarise this text in three sentences' is carried out reliably.

Fine-tuning
Language models
+1

Transfer Learning

In transfer learning, a model that has already learnt a large general task serves as the starting point for a new, specialised one. Instead of starting from scratch, it is adapted, for instance through fine-tuning, using comparatively little data of your own. This saves computing time and data, for example when a general image model is meant to learn to spot production defects in photos.

Machine learning
Training
+1

Foundation model

A foundation model is a large AI model pre-trained on very extensive, wide-ranging data that can then be used for many different tasks. The term was coined at Stanford University in 2021. Well-known examples include large language models such as Claude or GPT, as well as image models. Businesses usually build their applications on such models rather than training their own from scratch; the EU AI Act refers to general-purpose AI models.

Base model
Language models
+1