Quicktour

PEFT offers parameter-efficient methods for finetuning large pretrained models. The traditional paradigm is to finetune all of a model’s parameters for each downstream task, but this is becoming exceedingly costly and impractical because of the enormous number of parameters in models today. Instead, it is more efficient to train a smaller number of prompt parameters or use a reparametrization method like low-rank adaptation (LoRA) to reduce the number of trainable parameters.

This quicktour will show you PEFT’s main features and how you can train or run inference on large models that would typically be inaccessible on consumer devices.

Train

Each PEFT method is defined by a PeftConfig class that stores all the important parameters for building a PeftModel. For example, to train with LoRA, load and create a LoraConfig class and specify the following parameters:

• task_type: the task to train for (sequence-to-sequence language modeling in this case)
• inference_mode: whether you’re using the model for inference or not
• r: the dimension of the low-rank matrices
• lora_alpha: the scaling factor for the low-rank matrices
• lora_dropout: the dropout probability of the LoRA layers

from peft import LoraConfig, TaskType

peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)

Once the LoraConfig is setup, create a PeftModel with the get_peft_model() function. It takes a base model - which you can load from the Transformers library - and the LoraConfig containing the parameters for how to configure a model for training with LoRA.

Load the base model you want to finetune.

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/mt0-large")

Wrap the base model and peft_config with the get_peft_model() function to create a PeftModel. To get a sense of the number of trainable parameters in your model, use the print_trainable_parameters method.

from peft import get_peft_model

model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
"output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282"

Out of bigscience/mt0-large’s 1.2B parameters, you’re only training 0.19% of them!

That is it 🎉! Now you can train the model with the Transformers Trainer, Accelerate, or any custom PyTorch training loop.

For example, to train with the Trainer class, setup a TrainingArguments class with some training hyperparameters.

training_args = TrainingArguments(
    output_dir="your-name/bigscience/mt0-large-lora",
    learning_rate=1e-3,
    per_device_train_batch_size=32,
    per_device_eval_batch_size=32,
    num_train_epochs=2,
    weight_decay=0.01,
    eval_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True,
)

Pass the model, training arguments, dataset, tokenizer, and any other necessary component to the Trainer, and call train to start training.

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)

trainer.train()

Save model

After your model is finished training, you can save your model to a directory using the save_pretrained function.

model.save_pretrained("output_dir")

You can also save your model to the Hub (make sure you’re logged in to your Hugging Face account first) with the push_to_hub function.

from huggingface_hub import notebook_login

notebook_login()
model.push_to_hub("your-name/bigscience/mt0-large-lora")

Both methods only save the extra PEFT weights that were trained, meaning it is super efficient to store, transfer, and load. For example, this facebook/opt-350m model trained with LoRA only contains two files: adapter_config.json and adapter_model.safetensors. The adapter_model.safetensors file is just 6.3MB!

Inference

Easily load any PEFT-trained model for inference with the AutoPeftModel class and the from_pretrained method:

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
import torch

model = AutoPeftModelForCausalLM.from_pretrained("ybelkada/opt-350m-lora")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")

model = model.to("cuda")
model.eval()
inputs = tokenizer("Preheat the oven to 350 degrees and place the cookie dough", return_tensors="pt")

outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])

"Preheat the oven to 350 degrees and place the cookie dough in the center of the oven. In a large bowl, combine the flour, baking powder, baking soda, salt, and cinnamon. In a separate bowl, combine the egg yolks, sugar, and vanilla."

For other tasks that aren’t explicitly supported with an AutoPeftModelFor class - such as automatic speech recognition - you can still use the base AutoPeftModel class to load a model for the task.

from peft import AutoPeftModel

model = AutoPeftModel.from_pretrained("smangrul/openai-whisper-large-v2-LORA-colab")

Next steps

Now that you’ve seen how to train a model with one of the PEFT methods, we encourage you to try out some of the other methods like prompt tuning. The steps are very similar to the ones shown in the quicktour:

1. prepare a PeftConfig for a PEFT method
2. use the get_peft_model() method to create a PeftModel from the configuration and base model

Then you can train it however you like! To load a PEFT model for inference, you can use the AutoPeftModel class.

Feel free to also take a look at the task guides if you’re interested in training a model with another PEFT method for a specific task such as semantic segmentation, multilingual automatic speech recognition, DreamBooth, token classification, and more.