Llama 2 Fine Tuning Code

Fine-Tuning Llama 2: A Comprehensive Guide

Introduction

In this article, we will explore the detailed steps involved in fine-tuning the impressive Llama 2 model with 7 billion parameters on a T4 GPU. We will also provide an option for utilizing a free T4 GPU.

Step-by-Step Guide

To initiate the fine-tuning process, you will need to:

1. Prepare your training data and ensure it is in a compatible format.
2. Obtain a T4 GPU, either a dedicated one or a free one through services like Google Colab or Kaggle.
3. Install the necessary software and dependencies, including TensorFlow and the Metas Llama 2 package.
4. Create a TensorFlow model and load the Llama 2 weights.
5. Configure the fine-tuning parameters, such as the learning rate and batch size.
6. Train the model on your training data.
7. Evaluate the fine-tuned model on a held-out validation set.

Key Concepts

To grasp the fine-tuning process effectively, it is crucial to understand these key concepts:

• Supervised Fine-Tuning (SFT): This approach involves using labeled data to fine-tune the model.
• Reinforcement Learning from Human Feedback (RLHF): This method utilizes human feedback to guide the fine-tuning process.
• Prompt Templates: These are pre-defined text structures that guide the model's response during fine-tuning.

Conclusion

By following these steps and leveraging the key concepts discussed, you can successfully fine-tune the Llama 2 model to meet your specific requirements and enhance its performance on your own data.