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name: peft description: "Use PEFT for parameter-efficient fine-tuning adapters, LoRA and quantized workflows, prompt and soft methods, specialized tuners, save/load/merge operations, training integrations, and PEFT repository development." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PEFT

Use this skill when a task names Hugging Face PEFT, peft, PeftModel, PeftConfig, get_peft_model, LoRA/QLoRA, prompt tuning, adapter checkpoints, adapter merging, PEFT training integrations, or changes to the PEFT repository.

Route By Task

• Core adapter setup: use sub-skills/adapter-core/SKILL.md for PeftConfig, TaskType, PeftType, get_peft_model, adapter lifecycle, trainable status, custom model targeting, and low-level injection.
• LoRA and quantization: use sub-skills/lora-and-quantization/SKILL.md for LoraConfig, QLoRA, DoRA, rsLoRA, PiSSA, CorDA, LoftQ, QALoRA, trainable tokens, and quantized-base compatibility.
• Prompt and soft methods: use sub-skills/prompt-and-soft-methods/SKILL.md for prompt tuning, prefix tuning, P-tuning, multitask prompt tuning, CPT, adaption prompt, cartridge, and trainable-token prompt workflows.
• Specialized tuners: use sub-skills/specialized-tuners/SKILL.md for IA3, BOFT, OFT, LoHa, LoKr, VeRA/PVeRA, VBLoRA, XLora, FourierFT, HRA/HiRA/SHiRA, RandLoRA, and other non-LoRA/non-prompt method families.
• Save, load, merge, and deploy: use sub-skills/save-load-merge/SKILL.md for adapter checkpoint layout, save_pretrained, from_pretrained, AutoPeftModel*, merge_and_unload, hotswap, mixed adapters, Hub/local loading, and conversion.
• Training integrations: use sub-skills/training-and-integrations/SKILL.md for Transformers Trainer, Accelerate, FSDP, DeepSpeed, TRL SFT, Diffusers examples, memory-efficient training, and torch.compile caveats.
• PEFT repository development: use sub-skills/repo-development/SKILL.md for contribution policy, new tuner registration, test selection, style/quality commands, docs, and AI-assisted PR disclosure rules.

Fast Start

1. Verify imports with sub-skills/adapter-core/scripts/check_peft_env.py in an environment that has peft, torch, transformers, and accelerate.
2. Decide the adapter family before writing training code. LoRA and quantized workflows have different constraints from prompt-learning and specialized tuners.
3. Keep base-model loading, adapter construction, training launcher settings, and checkpoint save/merge decisions separate so each can be reviewed.
4. Avoid downloading large models in smoke tests. Use config construction, helper scripts, or tiny custom torch.nn.Module checks when possible.

References

• references/workflow-overview.md gives a root-level decision map across adapter lifecycle stages.
• references/troubleshooting.md covers cross-cutting install/import, optional backend, target-module, checkpoint, merge, and training failures.
• references/repo-provenance.md records the source revision and extraction evidence.