PEFT — the adapter library

You know the methods (Topics 22–23); PEFT is Hugging Face's implementation of them, and you've already called it (LoraConfig, get_peft_model). What completes the picture is its lifecycle API — the verbs of adapter management:

from peft import PeftModel
 
model = PeftModel.from_pretrained(base, "you/your-adapter")  # attach
model.merge_and_unload()                                     # Topic 23's merge
model.load_adapter("you/other-adapter", adapter_name="b")    # wardrobe...
model.set_adapter("b")                                       # ...switch outfits

An adapter on disk is two tiny files — adapter_model.safetensors + adapter_config.json — which is why they're shareable, hot-swappable, and checkpoint-cheap (Topic 26's dividend). Beyond LoRA, the same LoraConfig carries the modern refinements as flags (use_dora=True for Topic 22's DoRA), and the library implements the rest of the family tree (IA³, prompt tuning) under one interface. One genuinely fun advanced verb: add_weighted_adapter — arithmetic between adapters, blending, say, 0.7 × your-style-adapter + 0.3 × json-adapter into a new one. Task vectors as a product feature.