Why it matters

PEFT democratizes fine-tuning. Understanding it unlocks cost-effective model customization.

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The architecture

Methods: LoRA (low-rank adaptation), QLoRA (quantized base + LoRA), Prefix Tuning, Prompt Tuning, IA3, Adapters.

Config: LoraConfig, PrefixTuningConfig, etc. Specify target modules, hyperparameters.

PEFT workflowBase modelfrozenPEFT methodsmall trainable paramsAdapter saved separatelyswap at inferenceAdapters are tiny (MB) vs full models (GB); swap without reloading base
PEFT model structure.
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How it works end to end

Application: get_peft_model(model, config) wraps base model with PEFT adapters.

Training: adapters trainable, base frozen. Optimizer only updates adapters.

Saving: adapter weights alone. Reload with base model.

Merging: merge_and_unload combines adapter into base for standalone use.