What is PEFT (Parameter-Efficient Fine-Tuning)

 

⚡ What is PEFT (Parameter-Efficient Fine-Tuning)

PEFT stands for Parameter-Efficient Fine-Tuning.
It is a technique and a library (by Hugging Face) that lets you fine-tune large language models without updating all their parameters, which makes training much faster and cheaper.

Instead of modifying the billions of weights in a model, PEFT methods only add or update a small number of parameters — often less than 1% of the model size.

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🧠 Why PEFT is Needed

Full Fine-Tuning	PEFT
Updates all parameters	Updates only a few parameters
Requires huge GPU memory	Needs much less memory
Slow and expensive	Fast and low-cost
Hard to maintain multiple versions	Easy to store/share small adapters

This is crucial when you want to:

• Customize big models (like LLaMA, Falcon, GPT-style models)

• Use small GPUs (even a single 8–16 GB GPU)

• Train multiple domain-specific variants

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⚙️ Types of PEFT Methods

The PEFT library by Hugging Face implements several techniques:

Method	Description
LoRA (Low-Rank Adaptation)	Adds small trainable low-rank matrices to attention layers
Prefix-Tuning	Adds trainable "prefix" vectors to the input of each layer
Prompt-Tuning / P-Tuning	Adds trainable virtual tokens (soft prompts) to the model input
Adapters	Adds small trainable feed-forward layers between existing layers
IA³ (Intrinsic Adaptation)	Scales certain layer activations with learnable vectors

💡 LoRA is the most commonly used PEFT method and works great for LLMs like LLaMA, Mistral, etc.

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🧪 Example Usage (Hugging Face PEFT library)

from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM

# Load base model
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")

# Configure LoRA (a PEFT method)
config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj","v_proj"], # only add LoRA to these layers
    lora_dropout=0.05,
    task_type="CAUSAL_LM"
)

# Apply PEFT
model = get_peft_model(model, config)

This trains only a few million LoRA parameters instead of billions.

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📌 Summary

PEFT is a set of methods (and a Hugging Face library) that make fine-tuning large models possible on small hardware by updating only a tiny fraction of their parameters.
It’s the standard approach today for customizing LLMs efficiently.