anshchoudhary / finetuning-llama2-7b

Fine-tuning the LLaMA 2-7B model (7 billion parameters) using QLoRA technique on a T4 GPU with 16 GB of VRAM.

Fine-tuning LLama-2-7B using QLoRA

This repository contains code and instructions for fine-tuning the LLaMA 2-7B language model on a custom dataset using the QLoRA (Quantized Low-Rank Adapters) technique.

What is Fine-tuning?

Fine-tuning is a transfer learning technique used to adapt a pre-trained language model to a specific task or domain. Instead of training a model from scratch, which requires a massive amount of data and computational resources, fine-tuning allows you to leverage the knowledge and patterns already learned by the pre-trained model and adapt it to your use case.

During fine-tuning, the pre-trained model's weights are adjusted (fine-tuned) using a smaller, task-specific dataset. This process allows the model to learn and specialize in the new task or domain while retaining its general language understanding capabilities.

The QLoRA Approach

Traditionally, fine-tuning involves updating all the model's weights, which can be computationally expensive and memory-intensive, especially for large language models like LLaMA-2-7B. QLoRA is a technique that addresses these challenges by introducing two key components:

1. Adapters: Instead of fine-tuning the entire model, QLoRA adds small adapter modules to the pre-trained model's layers. During fine-tuning, only the weights of these adapter modules are updated, while the pre-trained weights remain frozen. This significantly reduces the number of trainable parameters and computational requirements.

2. Quantization and Regularization: The weights of the adapter modules are quantized to low-bit precision (e.g., 4-bit) to further reduce the memory footprint. Additionally, Lorentz regularization is applied to the quantized adapter weights during training, which has been shown to improve performance.

By using QLoRA, you can efficiently fine-tune large language models like LLaMA-2-7B on new tasks or domains, achieving comparable or even better performance than full model fine-tuning while requiring significantly less computational resources and memory.

How to Fine-Tune LLaMA 2-7B: A Step-By-Step Guide

We will focus on fine-tuning the LLaMA 2-7B model (7 billion parameters) using a T4 GPU with 16 GB of VRAM. Due to the model's size, full fine-tuning is not feasible on such limited resources. Instead, we will employ a parameter-efficient fine-tuning technique called QLoRA (Quantized Lorentz-regularized Adapters).

QLoRA involves introducing small adapter modules to the pre-trained model's layers and updating only the weights of these adapters during fine-tuning. Additionally, the adapter weights are quantized to 4-bit precision to further reduce memory requirements, and Lorentz regularization is applied to improve performance.

To implement QLoRA fine-tuning, we will leverage the Hugging Face ecosystem of LLM libraries: transformers, accelerate, peft, trl, and bitsandbytes.

Repository Contents

This repository includes the following:

• data/: Directory for storing your fine-tuning dataset.
• scripts/: Python scripts for data preprocessing, fine-tuning with QLoRA, and evaluation.
• requirements.txt: List of required Python packages.
• README.md: This file, providing an overview of the project.

Getting Started

1. Clone this repository: git clone https://github.com/AnshChoudhary/finetuning-Llama2-7b.git
2. Install the required packages: pip install -r requirements.txt
3. Prepare your fine-tuning dataset and place it in the data/ directory.
4. Follow the instructions in the scripts/ directory to preprocess your data, fine-tune the LLaMA-2-7B model using QLoRA, and evaluate the fine-tuned model.

For detailed instructions and code examples, please refer to the documentation in the individual scripts and the repository's wiki.

Contributing

Contributions to this project are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.