Lesson 11: Custom Fine-Tuning

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In this lesson, we’ll explore the process of custom fine-tuning GPT and ChatGPT models. Fine-tuning allows you to adapt these powerful AI language models to better suit your specific needs, resulting in improved performance on your target tasks. We’ll cover the essential steps for preparing a custom dataset, fine-tuning the models, and evaluating the results of your fine-tuned models.

This lesson is part of The Prompt Artisan Prompt Engineering in ChatGPT: A Comprehensive Master Course.

11.1. Preparing a Custom Dataset

To fine-tune a GPT or ChatGPT model, you’ll first need a custom dataset that is representative of your target task. This dataset should ideally include diverse, high-quality examples that will help the model learn the nuances of your specific application. The process of preparing a custom dataset involves several key steps:

  • Data collection: Gather data that reflects the nature of your task. This can include text from domain-specific documents, websites, or existing databases. Ensure that you have permission to use the data and that it adheres to ethical considerations and privacy regulations.
  • Data preprocessing: Clean the data by removing irrelevant or redundant information, correcting errors, and standardizing formats. This step is crucial for ensuring that your dataset is of high quality and suitable for fine-tuning.
  • Data annotation: Annotate the data by labeling examples, providing ground truth information, or specifying the desired output. This step may require expert knowledge or collaboration with domain experts to ensure accurate annotations.
  • Data splitting: Divide the dataset into separate training, validation, and testing sets. The training set will be used for fine-tuning, the validation set for model selection and hyperparameter tuning, and the testing set for evaluating the model’s final performance.

11.2. Fine-tuning GPT and ChatGPT Models

Once you’ve prepared your custom dataset, you can proceed with fine-tuning your GPT or ChatGPT model. This process involves adjusting the model’s weights using your dataset to optimize its performance on your target task. Follow these steps to fine-tune your model:

  • Select a pre-trained model: Choose an appropriate GPT or ChatGPT model based on your requirements. Consider factors such as model size, inference speed, and resource constraints when making your decision.
  • Define the fine-tuning objective: Determine the specific goal you want the model to achieve, such as text classification, summarization, or question-answering. This will guide your fine-tuning process and help you establish appropriate evaluation metrics.
  • Configure hyperparameters: Set the appropriate learning rate, batch size, and number of training epochs for your fine-tuning process. These hyperparameters can significantly impact the performance of your fine-tuned model, so consider experimenting with different values and using the validation set to select the best configuration.
  • Fine-tune the model: Use your custom dataset to update the model’s weights. Be cautious of overfitting, which can occur if the model becomes too specialized to the training data and performs poorly on new, unseen data. Regularization techniques and early stopping can help mitigate overfitting.

11.3. Evaluating Fine-tuned Models

After fine-tuning your model, you’ll want to evaluate its performance on your target task. This involves using the testing set that you reserved during the data splitting process. Evaluating your fine-tuned model requires several key steps:

  • Select evaluation metrics: Choose appropriate metrics that align with your task and fine-tuning objectives. Common evaluation metrics include accuracy, precision, recall, F1 score, and ROUGE for summarization tasks. Remember that no single metric can perfectly capture a model’s performance, so consider using multiple metrics for a comprehensive evaluation.
  • Perform testing: Use your fine-tuned model to make predictions on the testing set. This set contains examples that the model has not seen during training or validation, allowing you to evaluate its performance on new, unseen data.
  • Compute evaluation scores: Compare the model’s predictions to the ground truth labels or outputs in the testing set. Calculate the selected evaluation metrics based on this comparison to quantify the performance of your fine-tuned model.
  • Analyze results: Examine the model’s performance across different evaluation metrics, and identify areas where it excels or struggles. This analysis can help you identify potential improvements to your fine-tuning process, dataset, or prompt engineering techniques.
  • Iterate and refine: If your fine-tuned model’s performance does not meet your expectations, consider refining your dataset, adjusting hyperparameters, or modifying your fine-tuning process. Repeat the fine-tuning and evaluation steps until you achieve the desired level of performance.

Examples: Preparing a Custom Dataset and Fine-tuning GPT-4 and ChatGPT Models

In this section, we’ll provide examples of how to prepare custom datasets and fine-tune GPT and ChatGPT models for different applications. We’ll consider three different use cases: sentiment analysis, summarization, and domain-specific question-answering.

Example 1: Sentiment Analysis with AI

Preparing a custom dataset:

  • Gather raw data: Collect movie or product reviews from online platforms.
  • Label data: Manually annotate the reviews with sentiment labels, such as “positive,” “negative,” or “neutral.”
  • Format data: Structure the data as JSON or CSV, including both the review text and the associated sentiment label.
  • Split data: Divide the data into training, validation, and testing sets (e.g., 80% for training, 10% for validation, and 10% for testing).

Fine-tuning GPT or ChatGPT models:

  • Define fine-tuning objectives: Train the model to predict the sentiment label based on the review text.
  • Select hyperparameters: Choose an appropriate learning rate, batch size, and number of training epochs for fine-tuning.
  • Perform fine-tuning: Fine-tune the model using the training set and monitor its performance on the validation set during the process.

Example 2: Domain-Specific Question-Answering (e.g., Medical AI)

Preparing a custom dataset:

  • Gather raw data: Collect medical-related questions and answers from authoritative sources, such as textbooks or expert forums.
  • Review data: Ensure the data is accurate and up-to-date, as misinformation in this domain could have severe consequences.
  • Format data: Structure the data as JSON or CSV, including both the question and the associated answer.
  • Split data: Divide the dataset into training, validation, and testing sets.

Fine-tuning GPT or ChatGPT models:

  • Define fine-tuning objectives: Train the model to generate accurate answers to domain-specific questions (in this case, medical questions).
  • Select hyperparameters: Choose an appropriate learning rate, batch size, and number of training epochs for fine-tuning.
  • Perform fine-tuning: Fine-tune the model using the training set and monitor its performance on the validation set during the process.

Example 3: Technical Support Dataset and Prompt

For the technical support scenario, let’s assume you’re working with a software company that wants to use GPT to help customers troubleshoot common issues. First, you’ll need to create a dataset with relevant questions and answers.

Dataset

The dataset can be organized into two columns: “Question” and “Answer”. Here’s a small sample of how the dataset could look:

Question,Answer
"How do I reset my password?","To reset your password, follow these steps: 1. Go to the login page. 2. Click on 'Forgot Password'. 3. Enter your email address. 4. Click 'Submit'. 5. Check your email for a password reset link. 6. Follow the instructions in the email to create a new password."
"Why is the software not responding?","If the software is not responding, try the following steps: 1. Close any unnecessary applications running on your computer. 2. Restart the software. 3. Update the software to the latest version. 4. Check your computer's system requirements to ensure compatibility. If the issue persists, contact our support team for further assistance."

Note how we are using a CSV format for this example. CSV formats are Comma Separated Values and are values included inside double quotes (“”) to represent a column value, while are separated by a comma (,) to separate the columns.

Prompt

Once the model is fine-tuned on the dataset, you can use a prompt like this to get a relevant response:

I'm having trouble with the software. It keeps crashing. What can I do to fix this?

Example 4: Recipe Recommendations Dataset and Prompt

For the recipe recommendation scenario, you need to create a dataset containing ingredients and their corresponding recipes. The dataset should be organized in a way that allows the model to understand the relationship between ingredients and recipes.

Dataset

The dataset can be organized into two columns: “Ingredients” and “Recipe”. Here’s a small sample (your should be of hundreds, thousands or more):

Ingredients,Recipe
"chicken, bell peppers, onion, garlic, soy sauce, sugar, cornstarch","Stir-Fried Chicken with Vegetables: 1. Cut chicken into bite-sized pieces. 2. Slice bell peppers and onion. 3. Mince garlic. 4. In a bowl, mix soy sauce, sugar, and cornstarch. 5. Heat oil in a pan. 6. Stir-fry chicken until cooked through. 7. Add vegetables and garlic, stir-frying until tender. 8. Add sauce mixture, stirring until thickened. 9. Serve with rice."
"ground beef, onion, garlic, tomato sauce, spaghetti, Parmesan cheese","Spaghetti Bolognese: 1. Brown ground beef in a large skillet. 2. Dice onion and mince garlic. 3. Add onion and garlic to beef, cooking until soft. 4. Stir in tomato sauce, simmering for 20 minutes. 5. Cook spaghetti according to package instructions. 6. Drain spaghetti and serve with sauce. 7. Top with Parmesan cheese."

Prompt

Once the model is fine-tuned, you can use a prompt like this to get a relevant response:

I have chicken breast, broccoli, and rice. Can you suggest a recipe using these ingredients?

Example 5: Medical Question-Answering Dataset and Prompt

For the medical question-answering scenario, you’ll need a dataset containing medical questions and their corresponding answers from reliable sources. The dataset should be organized in a way that allows the model to understand the relationship between questions and answers.

Dataset

The dataset can be organized into two columns: “Question” and “Answer”. Here’s a small sample:

Question,Answer
"What are the symptoms of the common cold?","Common cold symptoms include: 1. Runny or stuffy nose. 2. Sore throat. 3. Cough. 4. Sneezing. 5. Mild headache. 6. Body aches. 7. Low-grade fever. Symptoms usually appear one to three days after exposure to a cold-causing virus and typically last for 7 to 10 days."
"How can I prevent the spread of the flu?","To prevent the spread of the flu, follow these steps: 1. Get vaccinated annually. 2. Wash your hands frequently with soap and water, or use hand sanitizer. 3. Avoid close contact with people who are sick. 4. Cover your mouth and nose when coughing or sneezing. 5. Disinfect frequently-touched surfaces. 6. Stay home if you're feeling unwell."

Prompt

Once the model is fine-tuned on the dataset, you can use a prompt like this to get a relevant response:

What is the best way to treat a headache?

These examples demonstrate how to prepare custom datasets and fine-tune GPT and ChatGPT models for various applications. Remember to thoroughly evaluate your fine-tuned models using the testing set to ensure they perform well on unseen data. Additionally, consider reiterating the fine-tuning process and making adjustments to hyperparameters, dataset quality, or model architecture to further enhance the model’s performance.

By following these steps, you can create a custom fine-tuned GPT or ChatGPT model that is better suited to your specific application. Remember that fine-tuning is an iterative process, and it may take multiple attempts to achieve optimal results. Collaboration with domain experts, incorporating user feedback, and staying up-to-date with advances in GPT and ChatGPT technology can further enhance your fine-tuning efforts and contribute to the success of your project.

In the next lesson Lesson 12: Adapting Prompt Engineering for Domain-Specific Applications, we’ll explore how to adapt prompt engineering for domain-specific applications, including understanding domain-specific language, developing domain-specific prompts, and collaborating with domain experts.

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