How exactly was ChatGPT built? How can i build one too?

When OpenAI launched ChatGPT, with zero fanfare, in late November 2022, the San Francisco–based artificial-intelligence company had few expectations. Certainly, nobody inside OpenAI was prepared for a viral mega-hit. The firm has been scrambling to catch up—and capitalize on its success—ever since.

It was viewed in-house as a “research preview,” says Sandhini Agarwal, who works on policy at OpenAI: a tease of a more polished version of a two-year-old technology and, more important, an attempt to iron out some of its flaws by collecting feedback from the public. “We didn’t want to oversell it as a big fundamental advance,” says Liam Fedus, a scientist at OpenAI who worked on ChatGPT.

When you land on the OpenAI's website this is what you see. A very generic page showing just the basic information.

The only thing you should be knowing before moving further, is have basic understanding of how AI is developed. Lets dive in.

What is ChatGPT, and how does it work?

ChatGPT is an artificial intelligence-based service that you can access via the internet. You can use ChatGPT to organize or summarize text, or to write new text. ChatGPT has been developed in a way that allows it to understand and respond to user questions and instructions. It does this by “reading” a large amount of existing text and learning how words tend to appear in context with other words. It then uses what it has learned to predict the next most likely word that might appear in response to a user request, and each subsequent word after that. This is similar to auto-complete capabilities on search engines, smartphones, and email programs. - Official site.

Now, here is a step by step explanation on how you could develop similar models for your startup or clients. First we will understand the steps and then we will look into the technical code details.

A. The Concept

1. Research and Conceptualization

Objective Definition: The goal was to create a conversational AI that can generate human-like text and understand context, nuances, and complex language structures.

Feasibility Study: Researchers at OpenAI conducted extensive literature reviews on natural language processing (NLP), machine learning (ML), and deep learning (DL) techniques. The focus was on the Transformer architecture, introduced in the paper "Attention is All You Need" by Vaswani et al. (2017).

2. Data Collection and Preparation

Data Sourcing: A vast corpus of text data was collected from diverse sources, including books, websites, articles, and more. This diverse dataset helps in understanding various language patterns and contexts.

Data Cleaning: The collected data was preprocessed to remove noise:

• Tokenization: Text was broken down into tokens (words, subwords, characters) using tools like Byte Pair Encoding (BPE).
• Normalization: Text was standardized (e.g., lowercasing, removing special characters).
• Filtering: Irrelevant or harmful content was removed to ensure safe and relevant training data.

3. Model Design

Architecture Selection: The Transformer architecture was chosen due to its ability to handle long-range dependencies in text better than RNNs or LSTMs. The key components of the Transformer are:

• Self-Attention Mechanism: Allows the model to weigh the importance of different words in a sentence.
• Positional Encoding: Since Transformers don't have recurrence, positional encodings are added to give the model information about the position of words in a sequence.

Hyperparameter Tuning: Parameters such as the number of layers (e.g., 12 for GPT-2, 96 for GPT-3), hidden units, and attention heads were carefully selected. Hyperparameter tuning involves experimentation to balance model complexity and performance.

4. Training the Model

Pre-training:

• Objective: Train the model on a large dataset to learn general language patterns and structures. The model is trained using unsupervised learning to predict the next token in a sequence.
• Implementation: Training is distributed across multiple GPUs or TPUs to handle the large computations. Techniques like mixed-precision training are used to speed up training and reduce memory usage.
• Optimization: The Adam optimizer is used along with techniques like learning rate scheduling and gradient clipping to ensure stable and efficient training.

Fine-tuning:

• Objective: Adapt the pre-trained model to specific tasks or domains. This involves supervised learning on a smaller, task-specific dataset with human-annotated examples.
• Implementation: The model continues training on this new dataset while preserving the general language patterns learned during pre-training. This step ensures the model performs well on specific tasks like answering questions or engaging in dialogue.

5. Adversarial Training

Objective: Improve the model's robustness and security by exposing it to adversarial examples—inputs designed to fool the model.

Method:

• Generate Adversarial Examples: Create inputs that are slightly modified versions of the training data but intended to cause the model to make mistakes. Techniques include:
• Incorporate Adversarial Examples into Training:
• Adversarial Training Loop: During each training iteration, mix adversarial examples with regular training data. This forces the model to learn to correctly classify both clean and adversarial inputs.
• Regularization: Add regularization terms to the loss function to penalize large changes in the model's predictions for small input perturbations, enhancing robustness.

5. Evaluation and Iteration

Evaluation Metrics: Metrics such as perplexity (measuring how well the model predicts a sample), BLEU score (measuring similarity to human reference translations), and human evaluations are used to assess the model's performance.

Iterative Improvement: Based on evaluation results, the model is iteratively improved through:

• Hyperparameter adjustments.
• Additional fine-tuning with more data or different datasets.
• Experimenting with new training techniques such as curriculum learning or reinforcement learning from human feedback (RLHF).

6. Safety and Ethics

Bias Mitigation: Techniques to identify and reduce biases in the model are implemented. This involves:

• Analyzing model outputs for biased language and behaviors.
• Debiasing algorithms and controlled generation techniques.

Content Filtering: Develop and apply filters to prevent the model from generating harmful or inappropriate content. This involves:

• Regular expression filters.
• Custom content moderation rules.

Ethical Review: Conduct thorough ethical reviews to ensure compliance with ethical guidelines. This includes:

• Transparency about limitations.
• Guidelines for responsible use.

7. Deployment

Infrastructure Setup: Set up the necessary infrastructure to host the model. This includes:

• Cloud servers for scalability (e.g., AWS, Google Cloud, Azure).
• APIs for interaction allowing easy integration with other applications.

Optimization for Inference: Optimize the model for faster inference times. Techniques include:

• Model quantization: Reducing the precision of the model’s weights.
• Model pruning: Removing less important parts of the model.

Monitoring and Maintenance: Implement systems to monitor the model’s performance and ensure it operates within acceptable parameters. This includes:

• Regular updates to address new challenges.
• Ongoing maintenance to improve the model.

8. User Interaction and Feedback

User Interface: Design interfaces for user interaction such as:

• Web-based chat interfaces.
• Integrations with messaging apps.

Feedback Loop: Collect user feedback to further refine and improve the model. This involves:

• Analyzing user interactions.
• Identifying common failure modes.
• Iteratively incorporating feedback into the training and fine-tuning process.

Tools and Technologies Used

• Frameworks and Libraries: TensorFlow, PyTorch, Hugging Face Transformers.
• Datasets: Common Crawl, Wikipedia, BooksCorpus, and more.
• Cloud Platforms: AWS, Google Cloud, Azure.
• Pre-trained Models: Leveraging existing models (e.g., GPT-2, GPT-3) for quicker development and refinement.

B. The Tech

Workflow

1. Setup Environment

First, ensure you have the necessary libraries installed. We'll use torch, transformers, and other essential libraries.

pip install torch transformers datasets        

2. Data Collection and Preparation

Data Collection

For simplicity, let's assume we're using a dataset from the Hugging Face Datasets library, such as the "wikitext-2" dataset.

from datasets import load_dataset

dataset = load_dataset('wikitext', 'wikitext-2-raw-v1')        

Data Cleaning and Tokenization

We'll use the GPT-2 tokenizer to preprocess our text data.

from transformers import GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

def tokenize_function(examples):
    return tokenizer(examples['text'], padding='max_length', truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)        

3. Model Design

We'll use the pre-trained GPT-2 model from Hugging Face's Transformers library.

from transformers import GPT2LMHeadModel

model = GPT2LMHeadModel.from_pretrained('gpt2')        

4. Training the Model

Pre-training

We'll set up the training arguments and use the Trainer API to train our model.

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
    logging_dir='./logs',
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['validation']
)

trainer.train()        

5. Adversarial Training

Adversarial training involves creating adversarial examples and training the model on these examples to improve robustness.

import torch
import torch.nn.functional as F

def adversarial_example(model, inputs, targets, epsilon=0.1):
    inputs.requires_grad = True
    outputs = model(inputs)
    loss = F.nll_loss(outputs.logits, targets)
    model.zero_grad()
    loss.backward()
    data_grad = inputs.grad.data
    perturbed_inputs = inputs + epsilon * data_grad.sign()
    return perturbed_inputs

for epoch in range(training_args.num_train_epochs):
    for data in tokenized_datasets['train']:
        inputs = torch.tensor(data['input_ids']).unsqueeze(0)  # Add batch dimension
        targets = torch.tensor(data['input_ids']).unsqueeze(0)
        
        # Generate adversarial examples
        adv_data = adversarial_example(model, inputs, targets)
        
        # Train on adversarial examples
        outputs = model(adv_data)
        loss = F.nll_loss(outputs.logits, targets)
        loss.backward()
        trainer.optimizer.step()
        trainer.optimizer.zero_grad()        

6. Evaluation and Fine-tuning

Evaluate the model and perform fine-tuning as necessary.

results = trainer.evaluate()
print(f"Perplexity: {torch.exp(torch.tensor(results['eval_loss']))}")        

7. Safety and Ethics

Implement bias mitigation and content filtering techniques.

Bias Mitigation

Analyze outputs for biases and apply debiasing techniques.

# Example: Simple debiasing by post-processing outputs
def debias_output(output):
    biased_words = ['badword1', 'badword2']
    for word in biased_words:
        output = output.replace(word, '****')
    return output

# Generate text with debiasing
input_text = "Example input text"
generated_text = model.generate(torch.tensor(tokenizer.encode(input_text)).unsqueeze(0))
decoded_text = tokenizer.decode(generated_text[0])
print(debias_output(decoded_text))        

8. Deployment

Set up the necessary infrastructure for hosting the model.

Infrastructure Setup

Use a cloud platform like AWS, Google Cloud, or Azure. Here’s a simplified example of using a Flask API to serve the model:

from flask import Flask, request, jsonify
from transformers import pipeline

app = Flask(__name__)
conversational_pipeline = pipeline('conversational', model='gpt2')

@app.route('/generate', methods=['POST'])
def generate():
    input_text = request.json['text']
    response = conversational_pipeline(input_text)
    return jsonify(response)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)        

9. User Interaction and Feedback

Design user interfaces for interaction and collect feedback.

User Interface

You can create a simple web-based chat interface using HTML and JavaScript.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>ChatGPT Interface</title>
</head>
<body>
    <h1>Chat with GPT</h1>
    <div id="chat-box"></div>
    <input type="text" id="user-input" placeholder="Type your message here...">
    <button onclick="sendMessage()">Send</button>

    <script>
        async function sendMessage() {
            const userInput = document.getElementById('user-input').value;
            const response = await fetch('https://localhost:5000/generate', {
                method: 'POST',
                headers: {
                    'Content-Type': 'application/json'
                },
                body: JSON.stringify({ text: userInput })
            });
            const data = await response.json();
            document.getElementById('chat-box').innerHTML += `<p>User: ${userInput}</p><p>Bot: ${data[0]['generated_text']}</p>`;
        }
    </script>
</body>
</html>        

Feedback Loop

Analyze user interactions and improve the model iteratively.

# Example: Logging user interactions for analysis
@app.route('/generate', methods=['POST'])
def generate():
    input_text = request.json['text']
    response = conversational_pipeline(input_text)
    # Log the interaction
    with open('user_logs.txt', 'a') as log_file:
        log_file.write(f"User: {input_text}\nBot: {response[0]['generated_text']}\n")
    return jsonify(response)        

And you ready my friend, to start your own journey of building the ChatGPT like model and application.

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