🦙 LoRA & QLoRA Fine-Tuning Pipeline for LLaMA-2

This project demonstrates the implementation of parameter-efficient fine-tuning (PEFT) techniques — LoRA and QLoRA — for adapting the LLaMA-2 large language model using memory-efficient training strategies.

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📌 Project Overview

Large Language Models require significant computational resources for full fine-tuning.
This project explores modern efficient alternatives:

• LoRA (Low-Rank Adaptation)
• QLoRA (Quantized LoRA)

Both approaches reduce GPU memory usage while enabling model adaptation by training only a small number of additional parameters.

The notebooks focus on building a correct and reproducible fine-tuning pipeline using HuggingFace and PEFT libraries.

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📁 Project Structure

• Lora_Llama2.ipynb — LoRA fine-tuning setup for LLaMA-2
• QLoRA_Llama2.ipynb — QLoRA pipeline using quantized model loading
• README.md — Project documentation

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🧠 Concepts Demonstrated

• Parameter Efficient Fine-Tuning (PEFT)
• LoRA adapter configuration
• QLoRA 4-bit quantization workflow
• HuggingFace Transformers integration
• Memory-efficient LLM training setup

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⚠️ Execution Note

Due to hardware and GPU resource limitations, full fine-tuning was not executed in this repository.

The project focuses on correctly implementing and configuring the training pipelines required for LoRA and QLoRA fine-tuning. These notebooks are ready to run on suitable GPU environments such as:

• Google Colab (High-RAM / A100)
• Kaggle GPU sessions
• Local machines with ≥16GB VRAM

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🧠 Technologies Used

• Python
• HuggingFace Transformers
• PEFT (Parameter-Efficient Fine-Tuning)
• PyTorch
• BitsAndBytes (Quantization)
• LLaMA-2

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🔬 Why LoRA & QLoRA?

Method	Memory Usage	Trainable Parameters	Purpose
Full Fine-Tuning	Very High	100%	Expensive adaptation
LoRA	Low	Small adapters	Efficient tuning
QLoRA	Very Low	Small adapters	Consumer GPU training

QLoRA enables training large models even on limited hardware through 4-bit quantization.

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▶️ How to Run

1. Install dependencies text

pip install transformers peft accelerate bitsandbytes datasets

2. Open either notebook: text

- Lora_Llama2.ipynb
- QLoRA_Llama2.ipynb

3. Run in a GPU-enabled environment.

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🧪 Key Learnings

• Modern LLM training increasingly relies on PEFT methods
• Quantization dramatically reduces memory requirements
• LoRA enables efficient adaptation without retraining entire models
• Proper pipeline setup is critical before large-scale training

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🚀 Future Improvements

• Execute full fine-tuning on GPU hardware
• Compare LoRA vs QLoRA training efficiency
• Evaluate downstream task performance
• Integrate evaluation benchmarks

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