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.

What is the Transformers library

 

🤖 What is the Transformers library

Transformers is an open-source Python library by Hugging Face that provides:

• Pre-trained transformer models

• Easy APIs to load, train, and use them

• Support for tasks like text, vision, audio, and multi-modal AI

It is the most widely used library for working with LLMs (Large Language Models).

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⚙️ What it Contains

Here’s what the transformers library gives you:

🧠 Pre-trained models

• 1000+ ready-to-use models like:

  • GPT, BERT, RoBERTa, T5, LLaMA, Falcon, Mistral, BLOOM, etc.

• Downloaded automatically from the Hugging Face Hub

⚒️ Model classes

• AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM, etc.

• These automatically select the right architecture class for a model

📄 Tokenizers

• Converts text ↔ tokens (numbers) for the model

• Very fast (often implemented in Rust)

📦 Pipelines

• High-level API to run tasks quickly, for example:

  from transformers import pipeline
  generator = pipeline("text-generation", model="gpt2")
  print(generator("Once upon a time"))
  

🏋️ Training utilities

• Trainer and TrainingArguments for fine-tuning

• Works with PyTorch, TensorFlow, and JAX

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📊 Supported Tasks

Task	Example
Text Generation	Chatbots, storytelling
Text Classification	Spam detection, sentiment
Question Answering	QA bots
Translation	English → French
Summarization	Summarizing articles
Token Classification	Named entity recognition
Vision/Multimodal	Image captioning, VQA

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💡 Why It’s Popular

• Huge model zoo (open weights)

• Unified interface across models

• Active community and documentation

• Compatible with Hugging Face ecosystem: Datasets, Accelerate, PEFT (LoRA)

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📌 Summary

transformers is the go-to library for using and fine-tuning state-of-the-art AI models — especially large language models — with just a few lines of code.

What is LoRA (Low-Rank Adaptation)

 

LoRA is a parameter-efficient fine-tuning technique used to adapt large language models (LLMs) like LLaMA, GPT, etc., to new tasks without retraining the entire model.

Instead of updating all the billions of parameters, LoRA:

• Freezes the original model weights (keeps them unchanged)

• Inserts small trainable low-rank matrices into certain layers (usually attention layers)

• Only trains these small matrices, which are much smaller than the full model

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⚙️ How LoRA Works (Simplified)

Imagine an LLM has a large weight matrix W (like 4096×4096).

Normally, fine-tuning means updating all entries in W → which is huge.

With LoRA:

1. Keep W frozen.

2. Add two small matrices:

   • A (size 4096×r)

   • B (size r×4096) — where r is small (like 8 or 16)

3. Train only A and B.

4. At inference time, the effective weight becomes:

   W' = W + A × B
   

This drastically reduces the number of trainable parameters.

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📊 Why LoRA is Useful

Aspect	Full Fine-Tune	LoRA Fine-Tune
Parameters updated	All (billions)	Few million (<<1%)
GPU memory need	Very high	Very low
Training speed	Slow	Fast
Sharing	Must share full model	Just share small LoRA weights

This makes LoRA ideal when:

• You want to customize a big model on a small dataset

• You have limited GPU resources

• You want to train multiple variants of the same base model

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📦 Common Uses

• Domain-specific tuning (medical, legal, finance text)

• Instruction tuning or chat-like behavior

• Personalizing models for specific companies or users

• Combining with PEFT (Parameter-Efficient Fine-Tuning) frameworks like:

  • 🤗 Hugging Face PEFT

  • 🤖 bitsandbytes

  • 🦙 LLaMA + LoRA (common combo)

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📝 Summary

LoRA = a lightweight way to fine-tune large models by training only tiny "adapter" layers (low-rank matrices) while keeping original weights frozen.
It dramatically reduces cost, time, and storage needs for customizing LLMs.