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PEFT
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.
Overview
Overview
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.
Deployment
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.
Highlights
Supports multiple PEFT methods (LoRA, IA³, soft prompts, etc.)
Seamless integration with Transformers, Diffusers, and Accelerate
Compatible with quantization and CPU offloading for low‑resource training
Adapter checkpoints are only a few megabytes, saving storage
Pros
- Reduces GPU memory usage dramatically
- Maintains performance close to full fine‑tuning
- Small checkpoint size enables easy model versioning
- Runs on consumer‑grade hardware
Considerations
- Requires understanding of adapter configuration
- Limited to the PEFT methods implemented in the library
- Adds a small inference overhead due to adapter layers
- May need additional libraries (Accelerate) for distributed training
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Fit guide
Great for
- Researchers adapting LLMs with limited GPU resources
- Enterprises deploying many task‑specific models without storage blow‑up
- Developers targeting edge or consumer hardware
- Teams already using the Hugging Face ecosystem
Not ideal when
- Scenarios needing full weight updates for novel architectures
- Environments without Python or Hugging Face libraries
- Ultra‑low latency inference where extra adapter computation is prohibitive
- Projects requiring exhaustive hyperparameter search across all model weights
How teams use it
Sentiment analysis with a 12B LLM on a single A100
Achieves near‑full‑model accuracy while using under 10 GB GPU memory
Multilingual ASR fine‑tuning of Whisper large with LoRA + 8‑bit quantization
Enables real‑time transcription on a 16 GB GPU
SaaS product serving multiple tasks from one base model
Reduces per‑task storage to a few megabytes via adapters
Instruction‑template experimentation on T0‑3B using LoRA
Improves accuracy with minimal compute overhead
Tech snapshot
Python100%
Makefile1%
Dockerfile1%
Cuda1%
C++1%
Frequently asked questions
What is PEFT?
PEFT stands for Parameter‑Efficient Fine‑Tuning, a set of techniques that adapt large models by training only a small subset of added parameters.
How much of a model is typically trained with PEFT?
Often less than 0.2% of the total parameters, e.g., a few million trainable weights in a multi‑billion‑parameter model.
Can PEFT be combined with quantization?
Yes, PEFT adapters can be used together with 8‑bit or lower‑precision quantization to further reduce memory and compute.
Which libraries does PEFT integrate with?
PEFT works natively with Transformers, Diffusers, and Accelerate for training and inference.
How large are PEFT checkpoints compared to full model checkpoints?
PEFT adapters are typically a few megabytes, whereas full model checkpoints can be several gigabytes.
Project at a glance
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LicenseApache-2.0
Repo age3 years old
Last commit5 days ago
Primary languagePython
Last synced 12 hours ago