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PEFT
Fine-tune large models with lightweight adapters instead of full retraining
Apache-2.0
ABOUT
Full fine-tuning of large language, vision, speech, or diffusion models is expensive and often impractical for teams without large GPU budgets. PEFT lets developers adapt foundation models by training only small adapter layers, which cuts memory usage, lowers checkpoint size, and makes experimentation feasible on modest hardware.
INSTALL
pip install peftINTEGRATION GUIDE
1. Fine-tune LLMs on consumer GPUs with LoRA or QLoRA adapters
2. Train task-specific adapters instead of storing full copies of each model
3. Personalize diffusion models with lightweight DreamBooth-style adapter workflows
4. Add efficient fine-tuning to TRL, Transformers, and Accelerate training pipelines
5. Swap, merge, and manage multiple adapters on a shared base model
TAGS
pythonfine-tuninghuggingfaceloraqloraadapterspytorchtransformers