LLM Foundations: Constructing and Training Decoder-Only Transformers

In this article, we will guide you through building, training, and using a decoder-only Transformer model for text generation, inspired by GPT (Generative Pre-trained Transformer).

Transformer Architecture Overview

Above is an image showing the architecture of a full Transformer model, which includes both an encoder and a decoder. In this article, we will focus only on the decoder portion of the model.

The decoder is responsible for generating text sequences based on input embeddings and positional encodings, utilizing multi-head self-attention and feed-forward networks.

What's the plan?!

We’ll begin by creating a vocabulary from the text of “Pride and Prejudice,” (Great book btw!) stored in a .txt file, which maps unique words to numerical indices, making the text data usable by the model. Using the example sentence “The quick brown fox jumps over the lazy dog,” we’ll illustrate how the text is tokenized and converted into numerical sequences.

Next, we’ll define the TextDataset class for loading and preparing the text data, followed by the TransformerModel class, which implements the Transformer architecture. Each part of the model will be explained in detail using our example sentence to demonstrate the transformation of the input sequence at each stage.

We will then delve into the training loop, where the model learns by adjusting its parameters to minimize prediction error. Each step of the training process will be explained clearly.

Finally, we’ll cover saving the trained model to disk and loading it for later use, ensuring the model’s training progress is preserved. We’ll also show how to generate text using the trained model, starting from an initial sequence and predicting subsequent tokens.

By the end of this article, you’ll understand how to build, train, and use a decoder-only Transformer model for text generation, with practical examples provided throughout.

Library Imports and Installations

Before we dive into building and training our Large Language Model (LLM), we need to ensure we have the necessary libraries installed and imported. Below are the primary libraries you’ll need:

Installation

To install these libraries, you can use the following pip commands:

pip install torch tqdm colorama        

Imports

After installing the necessary libraries, you can import them in your Python script:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import math
from tqdm import tqdm
from colorama import Fore, Style, init

# Initialize colorama
init(autoreset=True)        

There's no model without data, let's get after that first!

Vocabulary in Transformer Models

A crucial step before feeding text data into any model, including Transformer models, is the creation of a vocabulary. The vocabulary serves as a bridge between raw text data and the numerical representations that the model can process.

What is a Vocabulary?

A vocabulary is a dictionary that maps each unique word in the text data to a unique numerical index. This transformation is necessary because machine learning models operate on numerical data, not raw text.

Why Do We Need a Vocabulary?

1. Numerical Representation: Models require inputs to be in numerical form. The vocabulary enables this transformation by assigning a unique index to each word.

2. Consistency: By mapping each word to a consistent index, the model can accurately learn and predict text sequences.

3. Handling Unknown Words: Special tokens like <unk> (unknown) help the model deal with words that were not seen during training.

4. Sequence Padding: Tokens like <pad> ensure that all input sequences are of the same length, which is important for batch processing.

5. Start and End Tokens: Tokens such as <sos> (start of sequence) and <eos> (end of sequence) help in demarcating the boundaries of text sequences, essential for tasks like text generation and translation.

In summary, the creation of a vocabulary is a foundational step in preparing text data for a Transformer model. It converts text into a format that the model can process and learn from, thereby enabling effective training and text generation.

Creating the Vocabulary

The first functional part of the script is the create_vocab function, which is used to create a vocabulary from the input text. This step is crucial as it converts the raw text into a format that can be processed by the model.

def create_vocab(text):
    words = text.split()
    unique_words = set(words)
    vocab = {word: i+4 for i, word in enumerate(unique_words)}
    vocab['<pad>'] = 0
    vocab['<unk>'] = 1
    vocab['<sos>'] = 2
    vocab['<eos>'] = 3
    return vocab        

The create_vocab function generates a vocabulary dictionary from the input text. Here's the detailed process and the math behind it:

1. Text Splitting:

    words = text.split()        

The input text is split into individual words based on whitespace. This creates a list of words.

2. Unique Words:

    unique_words = set(words)        

A set of unique words is created from the list. This removes duplicates and keeps only unique words.

3. Vocabulary Creation:

vocab = {word: i+4 for i, word in enumerate(unique_words)}
vocab['<pad>'] = 0
 vocab['<unk>'] = 1
vocab['<sos>'] = 2
vocab['<eos>'] = 3        

A dictionary is created that maps each unique word to a unique index, starting from

4. The indices 0, 1, 2, and 3 are reserved for special tokens: `<pad>`, `<unk>`, `<sos>`, and `<eos>`.

Mathematical Explanation:

1. Let V be the set of unique words.

2. The size of the vocabulary |V| is the number of unique words.

3. Each word w \in V is assigned an index i + 4 , where i is the position of the word in the set V .

4. Special tokens are assigned fixed indices:

<pad>: 0
<unk>: 1
<sos>: 2
<eos>: 3        

Example:

Let's go through a simple example to illustrate the process:

Input Text:

"The quick brown fox jumps over the lazy dog"

1. Text Splitting:

    words = ["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"]        

2. Unique Words:

    unique_words = {"The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"}        

3. Vocabulary Creation:

vocab = {
    "The": 4,
    "quick": 5,
    "brown": 6,
    "fox": 7,
    "jumps": 8,
    "over": 9,
    "the": 10,
    "lazy": 11,
    "dog": 12,
    "<pad>": 0,
    "<unk>": 1,
    "<sos>": 2,
    "<eos>": 3
}        

Mathematical Operations in Creating Vocabulary

1. Text Splitting:

- Operation: Splitting the text into tokens.

- Complexity: \( O(n) \), where \( n \) is the length of the text.

2. Unique Words Extraction:

- Operation: Creating a set from the list of words to remove duplicates.

- Complexity: \( O(n) \), where \( n \) is the number of words.

3. Vocabulary Dictionary Creation:

- Operation: Creating a dictionary with indices for each unique word.

- Complexity: \( O(|V|) \), where \( |V| \) is the size of the vocabulary.

By following these steps, the script prepares the dataset and sets up the constants required for training the Transformer model. The vocabulary creation ensures that the text data is converted into numerical format, which can be fed into the model for training.

After the creation of the vocabulary, the next step in the script is the implementation of the TextDataset class. This class is responsible for loading the text data, tokenizing it using the created vocabulary, and generating sequences of tokens that will be used for training the Transformer model.

TextDataset Class

The TextDataset class is a custom dataset class derived from `torch.utils.data.Dataset`. It handles the loading and preparation of the text data for the training process. To illustrate how this works, let’s use the example sentence “The quick brown fox jumps over the lazy dog.”

Here's the detailed implementation and explanation of the `TextDataset` class:

class TextDataset(Dataset):
    def __init__(self, filepath, vocab=None):
        with open(filepath, 'r', encoding='utf-8') as file:
            text = file.read().replace('\n', ' ')

        if vocab is None:
            self.vocab = create_vocab(text)
        else:
            self.vocab = vocab
        
        self.data = [self.vocab.get(word, self.vocab['<unk>']) for word in text.split()]
        self.data += [self.vocab['<eos>']] * MAX_SEQ_LENGTH
        
    def __len__(self):
        return len(self.data) - MAX_SEQ_LENGTH + 1

    def __getitem__(self, idx):
        sequence = self.data[idx:idx+MAX_SEQ_LENGTH]
        input_sequence = torch.tensor(sequence[:-1], dtype=torch.long)
        target_sequence = torch.tensor(sequence[1:], dtype=torch.long)
        return input_sequence, target_sequence        

Detailed Explanation

1. Initialization (`__init__` Method):

- File Reading: The text file is read, and newline characters are replaced with spaces. Suppose the text file contains the sentence “The quick brown fox jumps over the lazy dog.” The newline characters, if any, are replaced with spaces.

- Vocabulary Handling: If a vocabulary is provided, it is used; otherwise, a new vocabulary is created from the text.

- Tokenization: The text is split into words, and each word is mapped to its corresponding index from the vocabulary. Words not found in the vocabulary are mapped to the `<unk>` token. The text is split into words, and each word is mapped to its corresponding index from the vocabulary. For this example, the vocabulary might look like this:

vocab = {
    '<pad>': 0,
    '<unk>': 1,
    '<sos>': 2,
    '<eos>': 3,
    'The': 4,
    'quick': 5,
    'brown': 6,
    'fox': 7,
    'jumps': 8,
    'over': 9,
    'the': 10,
    'lazy': 11,
    'dog': 12
}        

- End of Sequence Tokens: To ensure each sequence ends properly, `<eos>` tokens are appended to the data.

def __init__(self, filepath, vocab=None):
    with open(filepath, 'r', encoding='utf-8') as file:
        text = file.read().replace('\n', ' ')
    if vocab is None:
        self.vocab = create_vocab(text)
    else:
        self.vocab = vocab
    self.data = [self.vocab.get(word, self.vocab['<unk>']) for word in text.split()]
    self.data += [self.vocab['<eos>']] * MAX_SEQ_LENGTH        

For our example sentence, the tokenized data might look like this:

self.data = [4, 5, 6, 7, 8, 9, 10, 11, 12, 3, 3, 3, ...]  # Assuming MAX_SEQ_LENGTH is sufficient        

2. Length Calculation (`__len__` Method):

- The length of the dataset is calculated as the total number of tokens minus the maximum sequence length plus one. This ensures that the dataset can be divided into sequences of the specified maximum length.

def __len__(self):
    return len(self.data) - MAX_SEQ_LENGTH + 1        

For our example, assuming MAX_SEQ_LENGTH = 10, the length of the dataset will be:

len(self.data) - 10 + 1 = (12 - 10 + 1) = 3        

3. Sequence Generation (`__getitem__` Method):

- For a given index, this method retrieves a sequence of length `MAX_SEQ_LENGTH` from the data.

- The sequence is split into an input sequence (all tokens except the last one) and a target sequence (all tokens except the first one).

- Both sequences are converted to PyTorch tensors.

   def __getitem__(self, idx):
    sequence = self.data[idx:idx+MAX_SEQ_LENGTH]
    input_sequence = torch.tensor(sequence[:-1], dtype=torch.long)
    target_sequence = torch.tensor(sequence[1:], dtype=torch.long)
    return input_sequence, target_sequence        

For index 0, the input and target sequences will be:

input_sequence = torch.tensor([4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=torch.long)
target_sequence = torch.tensor([5, 6, 7, 8, 9, 10, 11, 12, 3], dtype=torch.long)        

Summary:

The TextDataset class is essential for preparing the text data for the Transformer model. It:

- Loads the text data from a file.

- Uses the vocabulary to convert words into numerical indices.

- Generates input and target sequences for training.

- Handles the conversion of these sequences into tensors that can be fed into the model.

By encapsulating these operations in a dataset class, the script ensures that the data is efficiently and correctly processed, enabling effective training of the Transformer model.

TransformerModel Class

After creating the vocabulary and setting up the TextDataset class, the next step is to define the TransformerModel class. This class implements the decoder-only Transformer model for text generation.

Implementation

class TransformerModel(nn.Module):
    def __init__(self, vocab_size):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, EMBEDDING_SIZE)
        self.pos_encoder = PositionalEncoding(EMBEDDING_SIZE, MAX_SEQ_LENGTH)
        self.transformer_decoder_layer = nn.TransformerDecoderLayer(
            d_model=EMBEDDING_SIZE, nhead=NHEAD, dim_feedforward=FFN_HID_DIM
        )
        self.transformer_decoder = nn.TransformerDecoder(
            self.transformer_decoder_layer, num_layers=NUM_DECODER_LAYERS
        )
        self.fc_out = nn.Linear(EMBEDDING_SIZE, vocab_size)

    def generate_square_subsequent_mask(self, sz):
        mask = torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)
        return mask

    def forward(self, src):
        src_mask = self.generate_square_subsequent_mask(src.size(0)).to(src.device)
        src = self.embedding(src) * math.sqrt(EMBEDDING_SIZE)
        src = self.pos_encoder(src)
        output = self.transformer_decoder(tgt=src, memory=src, tgt_mask=src_mask)
        output = self.fc_out(output)
        return output        

Detailed Processing of the Sequence

When a sequence is processed through the TransformerModel, it undergoes several transformations. Let’s break down each step using the example sentence "The quick brown fox jumps over the lazy dog."

1. Embedding:

- Description: Each token in the sequence is converted into a dense vector of fixed size (`EMBEDDING_SIZE`).

- Example: For the input sequence [4, 5, 6, 7, 8, 9, 10, 11, 12] (corresponding to "The quick brown fox jumps over the lazy dog"), each token is converted into a 512-dimensional vector.

- Mathematical Representation: If E is the embedding matrix of size vocab_size x EMBEDDING_SIZE, then the embedding for a token `x_i` is `E[x_i]`.

What exactly does that mean....

? Think of E as a big table where each row represents a word in our vocabulary and each column represents a feature of that word.

? When we look up a word x_i in this table, we get a vector (a list of numbers) that represents that word in a way the model can understand.

input_sequence = torch.tensor([4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=torch.long)
embedding = model.embedding(input_sequence)        

2. Positional Encoding:

- Description: Positional encodings are added to the embeddings to provide information about the position of each token in the sequence.

- Example: After adding positional encodings, the embedding vectors for the sequence incorporate information about their positions.

- Mathematical Representation: If `PE` is the positional encoding matrix, the positionally encoded embedding for token `x_i` at position pos is `E[x_i] + PE[pos]`.

OK, please explain in simple terms!!

? Think of PE is another table, but instead of representing words, it represents positions (like first word, second word, etc.).

? We add the position vector from PE to our word vector from E to help the model understand where each word is in the sentence. This way, “dog” in “The quick brown fox jumps over the lazy dog” is different from “dog” in “dog jumps over the lazy fox”.

embedding_with_position = model.pos_encoder(embedding)        

3. Masking:

- Description: A mask is applied to prevent the model from attending to future tokens in the sequence. This is crucial for autoregressive models, which generate text token by token.

- Example: For a sequence of length 9, the mask ensures that the prediction for each token only considers the previous tokens.

- Mathematical Representation: The mask `M` is a matrix where `M[i, j] = -inf for j > I` and 0 otherwise.

OK, and what does that mean?

? The mask is a way to make sure the model doesn’t look ahead in the sentence when predicting the next word.

? For example, if we are predicting the second word, the mask will block out everything after the second word, so the model only “sees” the first word.

src_mask = model.generate_square_subsequent_mask(embedding_with_position.size(0))        

4. Transformer Decoder:

- Description: The masked sequence is passed through multiple layers of the Transformer decoder. Each layer includes multi-head self-attention and a feed-forward network.

- Example: The sequence [4, 5, 6, 7, 8, 9, 10, 11, 12] with positional encodings and mask is processed through the decoder layers.

- Mathematical Representation:

- Self-Attention: For each token x_i, attention scores are computed using the query, key, and value matrices. The output is a weighted sum of the value vectors.

- Feed-Forward Network: The output of the self-attention layer is passed through a feed-forward neural network.

Tell me in plain terms!

? Self-attention helps the model figure out which words in a sentence are important to each other.

? For example, in the sentence “The quick brown fox jumps over the lazy dog,” self-attention helps the model understand that “fox” and “jumps” are related.

? The model uses three tables (query, key, and value) to calculate attention scores and then combines the words based on these scores.

decoder_output = model.transformer_decoder(tgt=embedding_with_position,
memory = embedding_with_position, tgt_mask=src_mask)        

5. Output Layer:

- Description: The decoder’s output is transformed to match the vocabulary size. This produces logits, which are used to predict the next token in the sequence.

- Example: The final output for the input sequence [4, 5, 6, 7, 8, 9, 10, 11, 12] is a set of logits for each position, representing the probability distribution over the vocabulary.

- Mathematical Representation: If O is the output matrix of size EMBEDDING_SIZE x vocab_size, then the logits for token x_i are computed as O * output[x_i].

Okay, what?!...

? The output layer takes the processed information and translates it back into a list of probabilities for each word in our vocabulary.

? This is like the model saying, “Given the words I’ve seen so far, here’s how likely each word in the vocabulary is to come next.”

output_logits = model.fc_out(decoder_output)        

Putting It All Together

For the example sentence "The quick brown fox jumps over the lazy dog," let’s see how the input sequence is processed through the model:

1. Input Sequence: [4, 5, 6, 7, 8, 9, 10, 11, 12]

    input_sequence = torch.tensor([4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=torch.long)        

2. Embedding: Each token is converted into a 512-dimensional vector.

embedding = model.embedding(input_sequence)        

3. Positional Encoding: Positional information is added to these embeddings.

embedding_with_position = model.pos_encoder(embedding)        

4. Masking: The sequence is masked to prevent the model from looking ahead at future tokens.

src_mask = model.generate_square_subsequent_mask(embedding_with_position.size(0))        

5. Transformer Decoder: The masked sequence is passed through the decoder layers, which include multi-head self-attention and feed-forward networks.

decoder_output = model.transformer_decoder(tgt=embedding_with_position, memory=embedding_with_position, tgt_mask=src_mask)        

6. Output Layer: The decoder’s output is transformed to produce logits for each position, predicting the next token.

    output_logits = model.fc_out(decoder_output)        

Let's Summarize

The TransformerModel class defines the decoder-only Transformer architecture for text generation. It includes embedding layers, positional encoding, multi-head self-attention, and a feed-forward network. Using the example sentence "The quick brown fox jumps over the lazy dog," we illustrated how the model processes the input sequence and predicts the next token.

By defining and implementing this class, the script prepares the model for training on the dataset, enabling it to learn the style of the text and generate similar text sequences.

Next Step: Training the Model

After defining the TransformerModel class, the next step is to set up the training loop where the model learns from the dataset. This involves initializing the dataset and data loader, defining the loss function and optimizer, and running the training loop for a specified number of epochs.

Full Training Loop Code

for epoch in range(NUM_EPOCHS):
    total_loss = 0
    progress_bar = tqdm(enumerate(dataloader), total=len(dataloader), desc=f'Epoch {epoch+1}/{NUM_EPOCHS}', leave=True)
    for i, (src, tgt) in progress_bar:
        src = src.transpose(0, 1)
        tgt_output = tgt.transpose(0, 1)
        optimizer.zero_grad()
        output = model(src)
        loss = criterion(output.view(-1, len(dataset.vocab)), tgt_output.reshape(-1))
        loss.backward()
        optimizer.step()
        total_loss += loss.item()

        # Color-coded logging
        if loss.item() < 1.0:
            color = Fore.GREEN
        elif loss.item() < 2.0:
            color = Fore.YELLOW
        else:
            color = Fore.RED
        progress_bar.set_postfix(loss=f"{color}{loss.item():.4f}{Style.RESET_ALL}")
    avg_loss = total_loss / len(dataloader)
    print(f'End of Epoch {epoch+1}, Average Loss: {avg_loss:.4f}')        

Granular Detail Through Each Line of the Loop

1. Epoch Loop Start:

    for epoch in range(NUM_EPOCHS):        

Explanation: This line starts a loop that will run for a set number of epochs (complete passes through the dataset). Each epoch allows the model to see the entire dataset and update its parameters.

2. Initialize Total Loss:

    total_loss = 0        

Explanation: Before starting the training for this epoch, we set the total loss to 0. This will help us keep track of the cumulative loss for the epoch.

3. Progress Bar Setup:

 progress_bar = tqdm(enumerate(dataloader), total=len(dataloader), desc=f'Epoch {epoch+1}/{NUM_EPOCHS}', leave=True)        

Explanation: This sets up a progress bar to visualize the training progress. It will show how far along the current epoch is.

4. Batch Loop Start:

    for i, (src, tgt) in progress_bar:        

Explanation: This loop goes through each batch of data in the DataLoader. Each batch consists of a source sequence (`src`) and a target sequence (`tgt`).

5. Transpose Source and Target:

src = src.transpose(0, 1)
tgt_output = tgt.transpose(0, 1)        

Explanation: Transposing changes the shape of src and tgt_output to match the expected input format of the model. It's like rotating a matrix.

6. Zero the Gradients:

optimizer.zero_grad()        

Explanation: This clears any gradients that were calculated in the previous batch. This is important to prevent gradients from accumulating between batches.

7. Forward Pass:

output = model(src)        

Explanation: This line passes the source sequence through the model to get the predicted output. For our example sentence, it predicts the next word in the sequence.

- Example: If the source sequence is "The quick brown fox jumps over the lazy", the model will predict "dog".

8. Calculate Loss:

    loss = criterion(output.view(-1, len(dataset.vocab)), tgt_output.reshape(-1))        

Explanation: This calculates the loss by comparing the model's predictions to the actual target sequence. The loss measures how far off the model's predictions are from the actual words.

- Example: If the model predicts "dog" but the target is "cat", the loss will be higher.

9. Backward Pass:

    loss.backward()        

Explanation: This calculates the gradients of the loss with respect to the model's parameters. Gradients indicate how much each parameter should change to reduce the loss.

10. Update Parameters:

optimizer.step()        

Explanation: This updates the model's parameters using the gradients calculated in the backward pass. This step makes the model a bit better at predicting the next word.

11. Accumulate Total Loss:

total_loss += loss.item()        

Explanation: This adds the current batch's loss to the total loss for the epoch. This helps in tracking the model's performance over the entire epoch.

I like a nice display, even colored, when it comes to logging. Here's an example that portrays red, green and yellow.

12. Color-Coded Logging:

if loss.item() < 1.0:
    color = Fore.GREEN
elif loss.item() < 2.0:
    color = Fore.YELLOW
else:
    color = Fore.RED
progress_bar.set_postfix(loss=f"{color}{loss.item():.4f}{Style.RESET_ALL}")        

Explanation: This sets the color of the loss value in the progress bar based on the loss magnitude. It's a visual indicator of how well the model is performing. Green indicates good performance, yellow indicates moderate performance, and red indicates poor performance.

13. Calculate Average Loss:

    avg_loss = total_loss / len(dataloader)        

Explanation: This calculates the average loss for the epoch by dividing the total loss by the number of batches. This provides a summary of the model's performance for the epoch.

14. Print Average Loss:

print(f'End of Epoch {epoch+1}, Average Loss: {avg_loss:.4f}')        

Explanation: This prints the average loss for the epoch. It gives a sense of how much the model has improved (or not) after seeing the entire dataset once.

By following these steps, we can see how the model learns from the dataset, adjusts its parameters, and improves over time. This detailed, step-by-step explanation ensures clarity and understanding, especially for beginners.

Saving and Loading the Model

After training the model, the next step is to save the trained model to disk so that it can be loaded and used later without retraining. This is especially useful for deploying the model or resuming training later.

# Saving the model
torch.save(model.state_dict(), 'transformer_model.pth')

# Loading the model
model = TransformerModel(len(dataset.vocab))
model.load_state_dict(torch.load('transformer_model.pth'))
model.eval()        

Saving the Model

1. Save Model State:

- Description: Save the model's parameters (state_dict) to a file.

- Example: Save the trained model to a file named 'transformer_model.pth'.

torch.save(model.state_dict(), 'transformer_model.pth')        

Explanation:

- This line of code saves the model’s learned parameters to a file. The .state_dict() method returns a dictionary containing the model's parameters.

Loading the Model

2. Load Model State:

- Description: Load the model's parameters from a file.

- Example: Load the model parameters from the file 'transformer_model.pth'.

- Code:

model = TransformerModel(len(dataset.vocab))
model.load_state_dict(torch.load('transformer_model.pth'))
model.eval()        

Explanation:

- Initialize Model: Create an instance of the TransformerModel with the vocabulary size.

- Load Parameters: Load the saved parameters into the model using the .load_state_dict() method.

- Set to Evaluation Mode: Set the model to evaluation mode with .eval(). This is important because it disables certain layers like dropout, which are only used during training.

- Example: We load the trained parameters for the model that learned from the sentence "The quick brown fox jumps over the lazy dog."

Summarize

After training the model, we save the trained parameters to a file using torch.save(). Later, we can load these parameters back into a new instance of the model using torch.load() and continue using the model without retraining. This ensures that the model’s training progress is preserved and can be reused or deployed as needed.

In conclusion:

In this article, we’ve walked through the process of building, training, and utilizing a decoder-only Transformer model for text generation. Inspired by the architecture used in GPT (Generative Pre-trained Transformer), this model can generate coherent text sequences based on learned patterns from the training data.

We started by creating a vocabulary from the text of “Pride and Prejudice,” which involved mapping unique words to numerical indices. This step is crucial as it converts raw text into a numerical format that the model can process. Using the example sentence “The quick brown fox jumps over the lazy dog,” we demonstrated how text is tokenized and prepared for model training.

Next, we defined the TextDataset class to handle loading and preparing the text data. This class generates input and target sequences for the model, ensuring that the data is efficiently and correctly processed for training.

We then implemented the TransformerModel class, focusing on the decoder portion of the Transformer architecture. We explained each component, including embedding layers, positional encoding, multi-head self-attention, and the feed-forward network. Using our example sentence, we illustrated how the input sequence is transformed at each stage of the model.

The training loop was detailed step-by-step, showing how the model learns from the dataset by adjusting its parameters to minimize prediction error. Each line of code was explained to ensure clarity, especially for beginners.

We also covered saving the trained model to disk and loading it for future use. This step ensures that the model’s training progress is preserved, allowing it to be reused or deployed without retraining.

By following this comprehensive guide, you should now have a solid understanding of how to build, train, and use a decoder-only Transformer model for text generation. Whether you’re working on a personal project or looking to implement advanced text generation techniques in a professional setting, the concepts and examples provided here will serve as a valuable resource.

Thank you!