Parameter-Efficient Fine-Tuning (PEFT)
Definition
PEFT adapts models with comparatively few changed parameters.
Methods that adapt a pretrained model with relatively few additional or changed parameters.
Simply explained
Instead of rebuilding the entire AI component, only small parts or additional adjustments are trained.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A company trains small adapters instead of changing all model weights.
Limits and pitfalls
- Do not automatically portray PEFT as data-efficient, safe, or without quality loss.
Origin & history
Adapters, Prefix Tuning, and LoRA made PEFT widespread for large models.
Use in mid-sized companies
- Possible application: A company trains small adapters instead of changing all model weights.
Frequently asked questions
What does Parameter-Efficient Fine-Tuning mean?
Methods that adapt a pretrained model with relatively few additional or changed parameters. What mistakes should be avoided? Do not automatically portray PEFT as data-efficient, safe, or without quality loss.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: