peft

Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters. This significantly decreases the computational and storage costs. Recent state-of-the-art PEFT techniques achieve performance comparable to fully fine-tuned models.

PEFT is integrated with Transformers for easy model training and inference, Diffusers for conveniently managing different adapters, and Accelerate for distributed training and inference for really big models.

[!TIP] Visit the PEFT organization to read about the PEFT methods implemented in the library and to see notebooks demonstrating how to apply these methods to a variety of downstream tasks. Click the "Watch repos" button on the organization page to be notified of newly implemented methods and notebooks!

Check the PEFT Adapters API Reference section for a list of supported PEFT methods, and read the Adapters, Soft prompts, and IA3 conceptual guides to learn more about how these methods work.

Quickstart

Install PEFT from pip:

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_model. For the Qwen/Qwen2.5-3B-Instruct model, you're only training 0.1193% of the parameters!

import torch
from transformers import AutoModelForCausalLM
from peft import LoraConfig, TaskType, get_peft_model

device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
model_id = "Qwen/Qwen2.5-3B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device)
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    task_type=TaskType.CAUSAL_LM,
    # target_modules=["q_proj", "v_proj", ...]  # optionally indicate target modules
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# prints: trainable params: 3,686,400 || all params: 3,089,625,088 || trainable%: 0.1193

# now perform training on your dataset, e.g. using transformers Trainer, then save the model
model.save_pretrained("qwen2.5-3b-lora")

To load a PEFT model for inference:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
model_id = "Qwen/Qwen2.5-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device)
model = PeftModel.from_pretrained(model, "qwen2.5-3b-lora")

inputs = tokenizer("Preheat the oven to 350 degrees and place the cookie dough", return_tensors="pt")
outputs = model.generate(**inputs.to(device), max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# prints something like: Preheat the oven to 350 degrees and place the cookie dough in a baking dish [...]

Why you should use PEFT

There are many benefits of using PEFT but the main one is the huge savings in compute and storage, making PEFT applicable to many different use cases.

High performance on consumer hardware

Consider the memory requirements for training the following models on the ought/raft/twitter_complaints dataset with an A100 80GB GPU with more than 64GB of CPU RAM.

Model	Full Finetuning	PEFT-LoRA PyTorch	PEFT-LoRA DeepSpeed with CPU Offloading
bigscience/T0_3B (3B params)	47.14GB GPU / 2.96GB CPU	14.4GB GPU / 2.96GB CPU	9.8GB GPU / 17.8GB CPU
bigscience/mt0-xxl (12B params)	OOM GPU	56GB GPU / 3GB CPU	22GB GPU / 52GB CPU
bigscience/bloomz-7b1 (7B params)	OOM GPU	32GB GPU / 3.8GB CPU	18.1GB GPU / 35GB CPU

With LoRA you can fully finetune a 12B parameter model that would've otherwise run out of memory on the 80GB GPU, and comfortably fit and train a 3B parameter model. When you look at the 3B parameter model's performance, it is comparable to a fully finetuned model at a fraction of the GPU memory.

Submission Name	Accuracy
Human baseline (crowdsourced)	0.897
Flan-T5	0.892
lora-t0-3b	0.863

[!TIP] The bigscience/T0_3B model performance isn't optimized in the table above. You can squeeze even more performance out of it by playing around with the input instruction templates, LoRA hyperparameters, and other training related hyperparameters. The final checkpoint size of this model is just 19MB compared to 11GB of the full bigscience/T0_3B model. Learn more about the advantages of finetuning with PEFT in this blog post.

Quantization

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 easier to train and load LLMs for inference.

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