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Efficiently fine-tune large models with minimal parameters
PEFT enables state‑of‑the‑art parameter‑efficient fine‑tuning, cutting compute and storage while matching full‑model performance, integrated with Transformers, Diffusers, and Accelerate.
PEFT (Parameter‑Efficient Fine‑Tuning) provides a suite of methods—such as LoRA, IA³, and soft prompts—that adapt large pretrained models by training only a tiny fraction of parameters. This reduces GPU memory and storage requirements dramatically, often to under 0.2% of the original model, while delivering accuracy comparable to full fine‑tuning.
The library plugs directly into the Hugging Face ecosystem: wrap a base model with get_peft_model, train using the standard Trainer or Accelerate for distributed workloads, and save adapters that are only a few megabytes in size. PEFT adapters can be combined with quantization and CPU offloading to run on consumer‑grade hardware, making it practical to fine‑tune 12B‑parameter models on a single A100 or even a 16 GB GPU.
When teams consider PEFT, these hosted platforms usually appear on the same shortlist.
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Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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Improves accuracy with minimal compute overhead
PEFT stands for Parameter‑Efficient Fine‑Tuning, a set of techniques that adapt large models by training only a small subset of added parameters.
Often less than 0.2% of the total parameters, e.g., a few million trainable weights in a multi‑billion‑parameter model.
Yes, PEFT adapters can be used together with 8‑bit or lower‑precision quantization to further reduce memory and compute.
PEFT works natively with Transformers, Diffusers, and Accelerate for training and inference.
PEFT adapters are typically a few megabytes, whereas full model checkpoints can be several gigabytes.
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