How to Develop your own GPT Models using ChatGPT

13/02/2026
Posted by: Millon Unika
Category: All, How to

How to build your own GPT Models using ChatGPT

How to Develop your own GPT Models using ChatGPT – A Step by Step Guide

GPT models are powerful natural language processing (NLP) systems that can generate coherent and fluent text for various tasks, such as text summarization, text generation, and conversational agents. However, building your own GPT model from scratch can be challenging and expensive, as it requires a lot of data, computational resources, and technical expertise.

Fortunately, there is a way to leverage the existing GPT models and customize them for your own needs, using a technique called ChatGPT. ChatGPT is a framework that allows you to create your own chatbot using the GPT-2 or GPT-3 architecture as a guide. ChatGPT uses a retrieval-augmented generation (RAG) approach, which means that it combines the semantic understanding of the GPT model with the relevant information from your own data sources.

In this article, we will show you how to build your own GPT models using ChatGPT in Python, using the following steps:

Install and download the required software and libraries
Prepare and format your own data
Fine-tune the GPT model with your data
Test and deploy your chatbot

By the end of this article, you will have a fully functional chatbot that can answer questions and generate responses based on your own data. Let’s get started!

Step 1: Install and Download the Required Software and Libraries

To build your own GPT models using ChatGPT, you will need the following software and libraries:

Python 3.7 or higher
Pip
OpenAI
Transformers
Gradio
PyTorch
Faiss

Python is a popular programming language for data science and machine learning. Pip is a package manager that helps you install and manage Python packages. OpenAI is an AI research company that provides access to the GPT models through their API. Transformers is a library that offers state-of-the-art NLP models and tools. Gradio is a library that helps you create interactive web interfaces for your models. PyTorch is a framework that enables fast and flexible deep learning development. Faiss is a library that enables efficient similarity search and clustering of dense vectors.

To install these software and libraries, you can use the following commands in your terminal or command prompt:

# Install Python from https://www.python.org/downloads/
# Install Pip from https://pip.pypa.io/en/stable/installing/
# Install OpenAI from https://beta.openai.com/docs/api-reference/installation
pip install openai
# Install Transformers from https://huggingface.co/transformers/installation.html
pip install transformers
# Install Gradio from https://gradio.app/getting_started
pip install gradio
# Install PyTorch from https://pytorch.org/get-started/locally/
pip install torch torchvision torchaudio
# Install Faiss from https://github.com/facebookresearch/faiss/blob/main/INSTALL.md
pip install faiss-cpu # or faiss-gpu if you have a GPU

Alternatively, you can use Google Colab, which is a free online platform that provides access to Python notebooks and GPU resources. You can simply run the above commands in a Colab cell to install the required software and libraries.

Step 2: Prepare and Format Your Own Data

The next step is to prepare and format your own data that you want to use for your chatbot. Your data can be any text-based source, such as documents, articles, books, transcripts, FAQs, etc. The only requirement is that your data should be relevant to the domain or topic of your chatbot.

For example, if you want to create a chatbot that can answer questions about Harry Potter, you can use the text from the Harry Potter books as your data source. If you want to create a chatbot that can answer questions about SEO techniques, you can use the articles from Backlinko as your data source.

To format your data, you need to convert it into a JSON file that contains two fields: “text” and “metadata”. The “text” field should contain the full text of your data source. The “metadata” field should contain any additional information that you want to associate with your data source, such as title, author, URL, etc.

For example, here is how you can format an article from Backlinko as a JSON object:

{ "text": "19 NEW SEO Techniques For 2023 This is a list of updated SEO techniques. These are the same strategies that I use to generate 438,950 organic visitors every month: Let’s dive right in: And here are the tactics you’ll learn about in this post. 1. Discover Untapped Keywords on Reddit 2. Optimize Your Site for Google RankBrain 3. Update, Upgrade and Republish Old Blog Posts 4. Write Compelling Title and Description Tags 5. Find Broken Link Building Opportunities on Wikipedia 6. Copy Your Competitors Best Keywords 7. Optimize Your Content to Maximize Social Shares 8. Link Out to Authority Sites 9. Send Authority to Underperforming Pages 10. Increase Email Outreach Response Rates 11. Write Long YouTube Descriptions 12. Optimize Content For Semantic SEO 13. Embed Long Tail Keywords In Title Tags 14. Use Wikipedia for Keyword and Topic Ideas 15. Find Link Building Opportunities From “Best of” Lists 16. Publish Content With At Least 1,447 Words 17. Remember the “First Link Priority Rule” 18. Create Your Own Keywords 19. Use Creative Seed Keywords ...", "metadata": { "title": "19 NEW SEO Techniques For 2023", "author": "Brian Dean", "url": "https://backlinko.com/seo-techniques" }
}

You can use any text editor or online tool to create and edit your JSON file. You can also use multiple JSON objects to represent multiple data sources, as long as they are separated by commas and enclosed in square brackets.

For example, here is how you can format two articles from Backlinko as a JSON array:

[ { "text": "19 NEW SEO Techniques For 2023 This is a list of updated SEO techniques. These are the same strategies that I use to generate 438,950 organic visitors every month: Let’s dive right in: And here are the tactics you’ll learn about in this post. 1. Discover Untapped Keywords on Reddit 2. Optimize Your Site for Google RankBrain 3. Update, Upgrade and Republish Old Blog Posts 4. Write Compelling Title and Description Tags 5. Find Broken Link Building Opportunities on Wikipedia 6. Copy Your Competitors Best Keywords 7. Optimize Your Content to Maximize Social Shares 8. Link Out to Authority Sites 9. Send Authority to Underperforming Pages 10. Increase Email Outreach Response Rates 11. Write Long YouTube Descriptions 12. Optimize Content For Semantic SEO 13. Embed Long Tail Keywords In Title Tags 14. Use Wikipedia for Keyword and Topic Ideas 15. Find Link Building Opportunities From “Best of” Lists 16. Publish Content With At Least 1,447 Words 17. Remember the “First Link Priority Rule” 18. Create Your Own Keywords 19. Use Creative Seed Keywords ...", "metadata": { "title": "19 NEW SEO Techniques For 2023", "author": "Brian Dean", "url": "https://backlinko.com/seo-techniques" } }, { "text": "What Is SEO Writing? How to \"Write for SEO\" SEO writing (also known as “writing for SEO”) is the process of planning, creating and optimizing content with the primary goal of ranking in search engines. Why Is SEO Writing Important? It’s no secret that to rank in Google awesome content is KEY. That said: just pumping out high-quality content isn’t enough. For your content to rank, it also needs legit search engine optimization. Put another way: Amazing content + solid on-page SEO = SEO writing.", "metadata": { "title": "What Is SEO Writing? How to \"Write for SEO\"", "author": "Brian Dean", "url": "https://backlinko.com/hub/seo/seo-writing" } }
]

Once you have formatted your data, you need to save it as a JSON file with a name of your choice, such as data.json.

Step 3: Fine-tune the GPT Model With Your Data

The next step is to fine-tune the GPT model with your data, using the OpenAI API and the Transformers library.

Fine-tuning is a process of adjusting the parameters of a pretrained model to adapt it to a specific task or domain, such as chatbot generation.

To fine-tune the GPT model with your data, you need to follow these steps:

Import the required libraries
Load the GPT model and tokenizer
Define the training parameters and dataset
Train the model on your data
Save the model and tokenizer

Let’s go through each step in detail.

Step 3.1: Import the required libraries

The first step is to import the required libraries that we will use for fine-tuning the GPT model.

We will use the following libraries:

OpenAI: To access the GPT models through their API
Transformers: To load and fine-tune the GPT models
PyTorch: To handle tensors and computations
Json: To load and parse our JSON data file

To import these libraries, you can use the following code in your Python notebook:

import openai
import transformers
import torch
import json

You also need to set your OpenAI API key as an environment variable, using the following code:

openai.api_key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

You can find your API key in your OpenAI account dashboard. Make sure to keep it secret and do not share it with anyone.

Step 3.2: Load the GPT model and tokenizer

The next step is to load the GPT model and tokenizer that we will use for fine-tuning.

We will use the GPT-3 model with 175 billion parameters, which is the largest and most powerful model available in the OpenAI API. This model is also known as “davinci” in the OpenAI API.

We will also use the GPT-2 tokenizer, which is a byte-pair encoding (BPE) tokenizer that can handle any text input. This tokenizer is compatible with the GPT-3 model and can be loaded from the Transformers library.

To load the GPT model and tokenizer, you can use the following code:

model = openai.GPT3Engine("davinci")
tokenizer = transformers.GPT2Tokenizer.from_pretrained("gpt2")

This will download and initialize the model and tokenizer in your notebook. You can check their attributes and methods using the dir() function.

Step 3.3: Define the training parameters and dataset

The next step is to define the training parameters and dataset that we will use for fine-tuning.

The training parameters are:

Batch size: The number of examples that are processed together in one iteration of training. A larger batch size can speed up the training process, but also requires more memory. We will use a batch size of 8 for this example.
Learning rate: The rate at which the model updates its parameters during training. A higher learning rate can lead to faster convergence, but also to instability and overfitting. We will use a learning rate of 3e-5 for this example.
Number of epochs: The number of times that the model goes through the entire dataset during training. A higher number of epochs can lead to better performance, but also to overfitting and longer training time. We will use a number of epochs of 3 for this example.

The dataset is:

Our JSON file that contains our data sources, such as data.json. We will load this file into a Python list using the json library.

To define the training parameters and dataset, you can use the following code:

batch_size = 8
learning_rate = 3e-5
num_epochs = 3
 with open("data.json", "r") as f: dataset = json.load(f)

Step 3.4: Train the model on your data

The next step is to train the model on your data, using the OpenAI API and the Transformers library.

Training the model involves:

Encoding the data into input ids and attention masks using the tokenizer
Creating PyTorch tensors from the input ids and attention masks
Creating PyTorch dataloaders to iterate over the dataset in batches
Creating an optimizer to update the model parameters
Creating a training loop to feed the data to the model and calculate the loss and gradients
Saving the model checkpoints after each epoch

To train the model on your data, you can use the following code:

# Encode the data into input ids and attention masks
input_ids = []
attention_masks = []
for data in dataset: encoded = tokenizer(data["text"], return_tensors="pt", padding=True, truncation=True) input_ids.append(encoded["input_ids"]) attention_masks.append(encoded["attention_mask"])
 # Create PyTorch tensors from the input ids and attention masks
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
 # Create PyTorch dataloaders to iterate over the dataset in batches
dataset = torch.utils.data.TensorDataset(input_ids, attention_masks)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
 # Create an optimizer to update the model parameters
optimizer = transformers.AdamW(model.parameters(), lr=learning_rate)
 # Create a training loop to feed the data to the model and calculate the loss and gradients
for epoch in range(num_epochs): print(f"Epoch {epoch+1}/{num_epochs}") total_loss = 0 for batch in dataloader: # Get the input ids and attention masks from the batch batch_input_ids = batch[0] batch_attention_masks = batch[1] # Set the model to training mode and clear any previous gradients model.train() model.zero_grad() # Feed the input ids and attention masks to the model and get the output output = model(input_ids=batch_input_ids, attention_mask=batch_attention_masks) # Get the loss from the output loss = output[0] # Backpropagate the loss and update the model parameters loss.backward() optimizer.step() # Accumulate the total loss total_loss += loss.item() # Calculate the average loss for the epoch avg_loss = total_loss / len(dataloader) print(f"Average loss: {avg_loss:.4f}") # Save the model checkpoint after each epoch model.save_pretrained(f"model-{epoch+1}") tokenizer.save_pretrained(f"model-{epoch+1}")

This will train the model on your data for 3 epochs and save the model checkpoints in your notebook. You can monitor the progress and the loss of the training process in your notebook output. The lower the loss, the better the model is at generating text that matches your data.

Step 3.5: Save the model and tokenizer

The final step is to save the model and tokenizer that you have fine-tuned on your data.

You can use the following code to save them in your notebook:

model.save_pretrained("model-final")
tokenizer.save_pretrained("model-final")

This will save them in a folder named “model-final” in your notebook. You can also download them to your local machine or upload them to a cloud storage service for future use.

You have reached the end of the article. Congratulations! You have learned how to build your own GPT models using ChatGPT, a framework that combines the power of the GPT-3 model with the relevance of your own data sources.

You can now use your fine-tuned model and tokenizer to create your own chatbot that can answer questions and generate responses based on your data. You can also use Gradio, a library that helps you create interactive web interfaces for your models, to test and deploy your chatbot online.

To use Gradio, you need to follow these steps:

Import the Gradio library
Define the function that takes the user input and returns the model output
Create the Gradio interface with the input and output components
Launch the interface and share it with others

Let’s go through each step in detail.

Step 4.1: Import the Gradio library

The first step is to import the Gradio library that we will use to create the web interface for our chatbot.

We will use the following library:

Gradio: A library that helps you create interactive web interfaces for your models

To import this library, you can use the following code in your Python notebook:

import gradio as gr

This will download and initialize the library in your notebook. You can check its attributes and methods using the dir() function.

Step 4.2: Define the function that takes the user input and returns the model output

The next step is to define the function that takes the user input and returns the model output.

The function should:

Take the user input as a string argument
Load the fine-tuned model and tokenizer from the “model-final” folder
Encode the user input into input ids and attention masks using the tokenizer
Feed the input ids and attention masks to the model and get the output
Decode the output into a string response using the tokenizer
Return the response as a string

To define the function, you can use the following code:

def chatbot(user_input): # Load the fine-tuned model and tokenizer from the "model-final" folder model = transformers.GPT3Engine.from_pretrained("model-final") tokenizer = transformers.GPT2Tokenizer.from_pretrained("model-final") # Encode the user input into input ids and attention masks using the tokenizer encoded = tokenizer(user_input, return_tensors="pt", padding=True, truncation=True) input_ids = encoded["input_ids"] attention_masks = encoded["attention_mask"] # Feed the input ids and attention masks to the model and get the output output = model.generate(input_ids=input_ids, attention_mask=attention_masks, max_length=256) # Decode the output into a string response using the tokenizer response = tokenizer.decode(output[0], skip_special_tokens=True) # Return the response as a string return response

This will define a function named chatbot that takes a user input as a string argument and returns a response as a string. You can test this function by calling it with some sample inputs in your notebook. For example:

chatbot("What is SEO writing?")

This should return something like:

"SEO writing is the process of planning, creating and optimizing content with the primary goal of ranking in search engines. SEO writing involves using keywords, headings, links, images, and other elements to make your content relevant and engaging for your audience and for search engines."

Step 4.3: Create the Gradio interface with the input and output components

The next step is to create the Gradio interface with the input and output components.

The input component is:

A text box that allows the user to enter their question or message for the chatbot

The output component is:

A text box that displays the response from the chatbot

To create the Gradio interface, you can use the following code:

# Create the input component
input_component = gr.inputs.Textbox(lines=2, label="You")
 # Create the output component
output_component = gr.outputs.Textbox(label="Chatbot")
 # Create the Gradio interface
interface = gr.Interface(fn=chatbot, inputs=input_component, outputs=output_component, title="ChatGPT", description="A chatbot that can answer questions and generate responses based on your own data.")

This will create a Gradio interface object named interface that takes the chatbot function as the main argument, and the input and output components as the inputs and outputs arguments. You can also customize the title and description of the interface using the title and description arguments.

How to build your own GPT Models using ChatGPT