Fine-Tuning GPT-3.5 Turbo 🚀

This project demonstrates how to fine-tune OpenAI's GPT-3.5 Turbo model using Google Colab. By following the steps, you'll learn how to upload data, set up fine-tuning parameters, monitor the training process, and test the fine-tuned model.

📚 Project Overview

In this project, we go through the following key steps:

1. Install Dependencies: Set up the environment by installing the necessary libraries such as OpenAI and Google Colab integrations.
2. API Connection: Learn how to securely connect to the OpenAI API using your API key.
3. Prepare Data: Upload and prepare your training and validation data files for fine-tuning.
4. Fine-Tune the Model: Start a fine-tuning job using your uploaded data, specifying hyperparameters like epochs and batch size.
5. Monitor Progress: Learn how to monitor the fine-tuning job's status and handle interruptions gracefully.
6. Test the Fine-Tuned Model: Compare the performance of the fine-tuned model with the base model.

💡 Key Features

• Secure API Integration: Uses Google Colab's userdata module to securely access your OpenAI API key.
• Automated Fine-Tuning: Uploads training and validation files to OpenAI's servers for model fine-tuning.
• Signal Handling: Handles interruptions in case the Colab runtime is disconnected or the script is stopped.
• Monitor Training: Provides a way to track the training job's status and events in real-time.
• Compare Results: Test the fine-tuned model and compare its responses with the base model.

🛠 Requirements

• Google Colab (for running the notebook)
• OpenAI API key (for accessing the GPT-3.5 Turbo model)
• Google Drive (for storing data files)

🚀 Getting Started

1. Clone the Repository: Download or clone this repository to your local machine or Google Colab.
2. Install Dependencies: Follow the instructions in the notebook to install the required libraries.
3. Set Up API Key: Insert your OpenAI API key in the provided section in the notebook.
4. Prepare Your Data: Make sure your training and validation data are in JSONL format and upload them to Google Drive.
5. Run the Notebook: Follow the instructions in the notebook step-by-step to fine-tune the model.

👩‍💻 Author

Khaled Soudy.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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