What is PEFT (Parameter-Efficient Fine-Tuning) in Applied AI?
The umbrella term for fine-tuning techniques that update a small fraction of a model's parameters. LoRA, QLoRA, prefix tuning, and prompt tuning are common PEFT methods. PEFT trades some final-model quality for massive savings in compute, memory, and storage.
Why PEFT (Parameter-Efficient Fine-Tuning) Matters for Your AI or Cybersecurity Career
Hugging Face's peft library is the workhorse for production fine-tuning. AI engineering interviews test which method to reach for given a budget.
Related Applied AI Terms
The umbrella term for fine-tuning techniques that update a small fraction of a model's parameters. LoRA, QLoRA, prefix tuning, and prompt tuning are common PEFT methods. PEFT trades some final-model quality for massive savings in compute, memory, and storage.
Hugging Face's peft library is the workhorse for production fine-tuning. AI engineering interviews test which method to reach for given a budget.
Sources
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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