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Llama 2 Fine Tuning Tutorial

Fine-Tuning LLaMA 2: A Step-by-Step Guide

Introduction

In this comprehensive guide, we'll explore the process of fine-tuning LLaMA 2 using Google Colab. We'll provide step-by-step instructions and cover new methodologies to enhance your fine-tuning experience.

Prerequisites

To follow along, you'll need the following:

  • A Google Colab account
  • A dataset for fine-tuning
  • Basic understanding of Python programming

Step 1: Setting Up Google Colab

1. Visit Google Colab and sign in to your account.

2. Create a new notebook and paste the following code:

```python !pip install transformers import transformers ```

Step 2: Loading the Dataset

1. Upload your dataset to your Google Drive or a cloud storage service.

2. In your notebook, mount the drive or access the data from the cloud storage.

Step 3: Fine-Tuning the Model

1. Import the necessary Transformers and Hugging Face libraries.

2. Create a tokenizer and model.

3. Define the training configuration.

4. Train the model.

Step 4: Evaluating the Model

1. Load a test set.

2. Evaluate the model's performance.

Step 5: Deploying the Model

1. Export the fine-tuned model.

2. Deploy the model to a serving platform.

Conclusion

By following these steps, you can successfully fine-tune LLaMA 2 for various NLP tasks. Remember to experiment with different hyperparameters and methodologies to optimize the model's performance.


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