Llama 2 Fine Tuning

Fine-Tuning LLaMA-2 with Google Colab

Fine-tuning LLaMA-2 with QLoRA on a Custom Dataset

Introduction

In this detailed guide, we will walk you through the process of fine-tuning Meta's LLaMA-2 on a new dataset using Google Colab. We'll leverage the QLoRA fine-tuning method, combining quantization and LoRA for enhanced performance and efficiency.

Prerequisites

To follow along, you'll need the following:

• Google Colab account
• LLaMA-2 model or checkpoint
• Custom dataset

Step-by-Step Instructions

1. Import Libraries: Import necessary libraries such as torch, transformers, and datasets.
2. Load Data: Create a DataLoader for your custom dataset.
3. Load LLaMA-2: Load the pretrained LLaMA-2 model or checkpoint.
4. Define QLoRA Fine-tuning: Specify the QLoRA configuration, including the quantization method and LoRA parameters.
5. Fine-tune Model: Start the fine-tuning process, specifying parameters such as batch size, epochs, and learning rate.
6. Evaluate Performance: Measure the fine-tuned model's performance on a validation or test set.

Conclusion

By following this guide, you'll gain hands-on experience fine-tuning LLaMA-2 on your custom dataset. This technique allows you to leverage the power of LLaMA-2 while tailoring it for your specific needs, enhancing its accuracy and effectiveness.