The COMPEL Glossary Graph visualizes relationships between framework terminology, showing how concepts interconnect across domains, stages, and pillars. Term nodes cluster by pillar affiliation while cross-references reveal semantic dependencies — for example, how risk appetite connects to control effectiveness, model governance, and assurance requirements. This network representation helps practitioners navigate the framework vocabulary and understand that COMPEL terminology forms a coherent conceptual system rather than isolated definitions.
COMPEL Glossary / peft-parameter-efficient-fine-tuning
PEFT (parameter-efficient fine-tuning)
A family of fine-tuning techniques — most prominently LoRA, QLoRA, and adapters — that update only a small fraction of model parameters while freezing the rest.
What this means in practice
Achieves most of the quality benefit of full fine-tuning at a fraction of the compute and storage cost; widely used as the middle rung on the customisation ladder (RAG → few-shot → PEFT → full fine-tune).
Synonyms
parameter-efficient fine-tuning , LoRA , QLoRA , adapter tuning
See also
- Distillation — The training of a smaller "student" model to imitate a larger "teacher" model's behaviour — typically on a shared dataset of prompts and teacher outputs.
- Quantization (AI cost) — Representation of model weights (and sometimes activations) at lower numerical precision — INT8, INT4, or mixed-precision — to reduce memory footprint and accelerate inference.
- Fine-Tuning — Fine-tuning is the process of further training a pre-trained AI model on a specific dataset to adapt it for a particular task or domain.