What Is PEFT? A Guide to Parameter-Efficient Fine-Tuning
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A technical guide comparing LoRA, QLoRA, rsLoRA, AdaLoRA, DoRA, IA³, prompt tuning, and adapter deployment workflows. DEHA Research · July 16, 2026 · 18 min read PEFT, or Parameter-Efficient Fine-Tuning, is a family of methods that adapts a large pretrained model to new tasks by training only a small subset of its parameters or lightweight components added to the model, rather than modifying all of its weights. The objective is not merely to save GPU memory. PEFT also makes it possible to…
1Key Takeaways
- A technical guide comparing LoRA, QLoRA, rsLoRA, AdaLoRA, DoRA, IA³, prompt tuning, and adapter deployment workflows.
- DEHA Research · July 16, 2026 · 18 min read PEFT, or Parameter-Efficient Fine-Tuning, is a family of methods that adapts a large pretrained model to new tasks by training only a small subset of its parameters or lightweight components added to the model, rather than modifying all of its weights.
- The objective is not merely to save GPU memory.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that a technical guide comparing LoRA, QLoRA, rsLoRA, AdaLoRA, DoRA, IA³, prompt tuning, and adapter deployment workflows.
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