Master Fine-tuning GPT-3.5-TURBO

Master Fine-tuning GPT-3.5-TURBO

Table of Contents

1. Introduction
2. What is Fine Tuning?
3. When to Use Fine Tuning?
4. Pros and Cons of Fine Tuning
5. Requirements for Fine Tuning
6. Step 1: Prepare a CSV File
7. Step 2: Adding a Prompt Column
8. Step 3: Prepare the Messages
9. Step 4: Validate the Data
10. Step 5: Start Fine Tuning
11. Step 6: Download the Result File
12. Step 7: Graph the Results
13. Step 8: Manual Inspection of the Data
14. Conclusion

Fine Tuning: A Comprehensive Guide

Fine tuning is an important technique to improve the performance of machine learning models, including the popular GPT-3.5 turbo model using open AI's API. In this guide, we will walk You through the process of fine tuning in seven simple steps. We will also discuss when to use fine tuning, the requirements for fine tuning, and provide a detailed walkthrough for each step. By the end of this guide, you will have the knowledge and tools to fine tune your own models effectively.

1. Introduction

Machine learning models like GPT-3.5 turbo have shown impressive capabilities in generating text. However, they may not always produce desired results out of the box. Fine tuning is a technique that allows us to customize these models for specific tasks, styles, or tones.

2. What is Fine Tuning?

Fine tuning refers to the process of further training a pre-trained model on specific data to make it more accurate and effective for a particular task. It involves providing additional training data and using that data to update the model's parameters. By refining the model's understanding of specific Patterns and contexts, fine tuning helps in achieving better performance.

3. When to Use Fine Tuning?

Fine tuning can be useful in various scenarios. It is particularly effective when:

• The model needs to write in a certain style or tone.
• Structured answers are required, such as generating responses in the form of an array with specific attributes.
• General Prompts are not producing satisfactory results.
• Long prompts are not yielding desired outputs.

However, fine tuning may not be suitable for memorizing data. In such cases, it is better to rephrase the information within a prompt or utilize a vector embedding database.

4. Pros and Cons of Fine Tuning

Like any technique, fine tuning has its advantages and limitations. Let's take a closer look at the pros and cons:

Pros:

• Allows customization of pre-trained models for specific tasks.
• Improves the accuracy and performance of models.
• Helps in achieving desired style, tone, or structured answers.

Cons:

• Requires additional training data.
• Fine tuning can be time-consuming and computationally expensive.
• Not suitable for memorizing data.

It is important to weigh these pros and cons when considering fine tuning for your specific application.

5. Requirements for Fine Tuning

Before we begin the fine-tuning process, there are a few requirements that need to be met:

1. CSV File: Prepare a CSV file containing at least one column of training data. It is recommended to have a minimum of 10 examples, with ideally around 100 for better training results.

2. Open API Key: Obtain an open AI API key by following the instructions provided by Open AI. This key will be required for interacting with the API during the fine-tuning process.

In the following steps, we will walk you through the process of fine tuning using these requirements. Let's get started!

6. Step 1: Prepare a CSV File

The first step in the fine-tuning process is to prepare a CSV file. The file should contain the training data in at least one column. Each row should represent the content of a specific example. It is recommended to have a minimum of 10 examples, with a recommended minimum of 15 to allow for a training and test split.

7. Step 2: Adding a Prompt Column

In some cases, you may already have a prompt column in your CSV file, especially if you have a question-answer style dataset. However, if your data is in the form of blogs or tweets, you may not have a specific question that leads to each example. In such cases, you can use a technique called "rewinder" to generate a prompt that would lead to a specific example.

To add a prompt column, you can use a script like "rewinder.py." This script will send each row of your data to the Open AI API and ask it to generate a prompt that would be appropriate for generating the following content. The generated prompt can then be added as a Second column in your CSV file.

8. Step 3: Prepare the Messages

To utilize the Open AI API, we need to prepare the messages in a format that can be sent to the API. We will use another script called "messages.py" to convert the CSV file into the required format. During this process, you will be asked to provide a system message, which is recommended to include for better inference. Additionally, you can choose to split out a test data set for evaluation purposes.

The messages will include a system message, a user message (prompt), and an assistant message (content). This format will be used for both the training and test data sets.

9. Step 4: Validate the Data

Before proceeding further, it is essential to validate the data set to avoid any potential errors. Using the "validate.py" script, the data will be checked for errors, and the distribution of tokens in each message will be shown. Additionally, the estimated cost of fine-tuning will be provided.

10. Step 5: Start Fine Tuning

Once the data set has been validated, we can proceed with the fine-tuning process. Ensure that you have the Open AI API key handy. Running the script "fine_tune.py" will present you with options, and you should select the option to Create a fine-tuning job. This will initiate the training process by uploading the training and test data sets.

The fine-tuning job may take a few minutes to an hour, depending on the complexity of the model and the size of the data set. You can monitor the progress of the job by retrieving the list of fine-tuning jobs.

11. Step 6: Download the Result File

Once the fine-tuning job is complete, you will receive an email from Open AI with the fine-tuning job number and model number. Using this information, you can download the result file by running the script "fine_tune.py" and selecting the option to download and save the result file.

12. Step 7: Graph the Results

To Visualize the results of the fine-tuning process, you can run the script "graph.py" and provide the result file. This will generate a graph depicting the training and validation loss. Ideally, the validation loss should be decreasing, indicating an improvement in the fine-tuned model's performance.

13. Step 8: Manual Inspection of the Data

For a more detailed evaluation, you can use the script "evaluation.py" to randomly select a row from the test data set and compare the responses generated by the raw GPT model and the fine-tuned model. This manual inspection will help you determine the effectiveness of the fine-tuning process and make necessary adjustments, such as adding more data or refining the data set.

14. Conclusion

Fine tuning is a powerful technique for customizing machine learning models like GPT-3.5 turbo. By following the steps outlined in this guide, you can effectively fine tune your models to achieve better performance, style, and tone. Remember to consider the pros and cons of fine tuning and tailor the process to your specific requirements. Utilize the provided scripts and guidelines to optimize your fine-tuning process and unlock the full potential of your models.