PEFT adapts large pretrained models by training only a small number of additional parameters (e.g., via LoRA), significantly decreasing computational and storage costs. It operates as a Python library requiring a pretrained base model and coding to configure, wrap, and train or run inference.
Project overview
It reduces checkpoint sizes from gigabytes to a few megabytes (e.g., 19MB vs 11GB), making large model adaptation more resource-efficient.
Project type
Model Development · Infrastructure
Use cases
Image Processing
Deployment
Refer to project documentation
License
Apache-2.0
Best for
AI engineers, developers, and data teams performing model-training-customization who need to decrease computational and storage costs.
Key capabilities
Configures models for parameter-efficient fine-tuning by wrapping a base model with get_peft_model to train a tiny fraction of parameters.
Loads and runs inference on a base model with a PEFT adapter applied using PeftModel.
Creates model checkpoints that are only a few MBs in size instead of GBs.
Combines PEFT methods with quantization to make it easier to train and load LLMs for inference.
Limitations and risks
The Transformers integration does not include all functionalities offered in PEFT, such as methods for merging the adapter into the base model.
Getting started
Install easily via pip as a standard Python library. The first success path is to run pip install peft, then wrap your model with get_peft_model in Python.
Evidence and sources
README: Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretraine…
README: Install PEFT from pip: ```bash pip install peft ``` Prepare a model for training with a PEFT method such as LoRA by wrapping the base model and PEFT configuration with `get_peft_m…
README: To load a PEFT model for inference: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel
README: In many cases, you're only finetuning a very small fraction of a model's parameters and each checkpoint is only a few MBs in size (instead of GBs).
README: Quantization is another method for reducing the memory requirements of a model by representing the data in a lower precision. It can be combined with PEFT methods to make it even…