Step-by-Step Guide to Building Your Large Language Models (LLMs)

A step-by-step guide on creating your first Large Language Model (LLM), even if you’re new to natural language processing.

Imagine diving into the world of language models like an artist beginning a new painting on a blank canvas. In this scenario, the canvas is the boundless potential of Natural Language Processing (NLP), and your paintbrush is your grasp of Large Language Models (LLMs). This article is here to help you, a data practitioner who is new to NLP, to build your very first Large Language Model from the ground up. We’ll focus on the Transformer architecture and use tools like TensorFlow and Keras to achieve this.

What is a Large Language Model?

A large language model (LLM) is a type of artificial intelligence (AI) program that uses machine learning to recognize and generate language. LLMs are trained on large amounts of data, often from the internet, to understand human language and other complex data. They can then perform a variety of natural language processing (NLP) tasks, such as:

• Text generation: Responding to input with plausible text
• Summarization: Summarizing articles
• Question answering: Answering questions
• Text classification: Classifying text
• Translation: Translating text
• Prediction: Making predictions
• Content generation: Generating other types of content

What is LLMs?

Key Characteristics of Large Language Models:

1. Large Scale: LLMs, like GPT-3, BERT, and T5, contain billions of parameters and are trained on vast, diverse datasets from books, websites, and more.
2. Understanding Context: Unlike earlier models, LLMs comprehend entire sentences or paragraphs, capturing nuances and ambiguities in language.
3. Generating Human-Like Text: LLMs excel at producing text that resembles human writing, including completing sentences, writing essays, creating poetry, and generating code.
4. Adaptability: These models can be fine-tuned for specific tasks such as answering questions, translating languages, summarizing texts, or generating domain-specific content.

The Transformer: The Engine Behind LLMs

The Transformer architecture, introduced in “Attention Is All You Need” by Vaswani et al. (2017), is central to most LLMs. It functions like an advanced orchestra, with layers and attention mechanisms working together to understand and generate language.

Transformer architecture

Several key components and concepts:

1. Self-Attention Mechanism: The core idea allows the model to weigh dependencies between words regardless of sequence position. Uses scaled dot-product attention for efficient computation.
2. Multi-Head Attention: Utilizes multiple attention heads to capture different relationships between words, enhancing model capability.
3. Feed-Forward Neural Networks: Independently processes each sequence position through a two-layer feed-forward network with ReLU activation.
4. Positional Encoding: Augments input embeddings with positional information, crucial since Transformers do not inherently understand word order.
5. Encoder-Decoder Structure: Consists of an encoder for input processing and a decoder for output generation, facilitating tasks like translation and summarization.
6. Layer Normalization and Residual Connections: Stabilizes and accelerates training through residual connections and layer normalization.

Building the Transformer with TensorFlow and Keras

Setting Up Your Environment

Before diving into code, ensure you have TensorFlow installed in your Python environment:

pip install tensorflow

The Transformer model consists of encoders and decoders. Think of encoders as scribes, absorbing information, and decoders as orators, producing meaningful language.

Encoder Layer:

The TransformerEncoderLayer class implements one layer of the Transformer encoder architecture. It consists of:

• Multi-head self-attention mechanism (MultiHeadAttention).
• Feed-forward neural network (Dense layers with ReLU activation).
• Layer normalization and dropout for stabilization and regularization.

This layer is designed to be stacked multiple times to form the Transformer encoder, which is widely used in natural language processing tasks like machine translation and language modeling due to its ability to handle long-range dependencies effectively. Each TransformerEncoderLayer processes its input independently but uses residual connections and layer normalization to maintain information flow and stabilize gradients during training.

import tensorflow as tf
from keras.layers import MultiHeadAttention, LayerNormalization, Dense

class TransformerEncoderLayer(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(TransformerEncoderLayer, self).__init__()
        self.mha = MultiHeadAttention(num_heads, d_model)
        self.ffn = tf.keras.Sequential([
            Dense(dff, activation='relu'), 
            Dense(d_model)
        ])

        self.layernorm1 = LayerNormalization(epsilon=1e-6)
        self.layernorm2 = LayerNormalization(epsilon=1e-6)
        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)
    
    def call(self, x, training):
        # Attention and Feed-Forward Operations:
        attn_output = self.mha(x, x, x)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(x + attn_output)

        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layernorm2(out1 + ffn_output)

        return out2

Decoder Layer:

The Transformer decoder is a crucial part of the Transformer model, designed for tasks that require generating outputs based on the context learned from input sequences (like machine translation).

It uses multi-head attention mechanisms (mha1 and mha2) to handle both self-attention within the decoder and attention over the encoder's outputs. These are followed by feed-forward networks and layer normalization, ensuring stable training and effective information flow through the network. By stacking multiple decoder layers, each performing these operations, the Transformer achieves state-of-the-art performance in various natural language processing tasks.

class TransformerDecoderLayer(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(TransformerDecoderLayer, self).__init__()
        self.mha1 = MultiHeadAttention(num_heads, d_model)
        self.mha2 = MultiHeadAttention(num_heads, d_model)

        self.ffn = tf.keras.Sequential([
            Dense(dff, activation='relu'), 
            Dense(d_model)
        ])

        self.layernorm1 = LayerNormalization(epsilon=1e-6)
        self.layernorm2 = LayerNormalization(epsilon=1e-6)
        self.layernorm3 = LayerNormalization(epsilon=1e-6)
        
        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)
        self.dropout3 = tf.keras.layers.Dropout(rate)

    def call(self, x, enc_output, training, look_ahead_mask, padding_mask):
        attn1, attn_weights_block1 = self.mha1(x, x, x, look_ahead_mask)
        attn1 = self.dropout1(attn1, training=training)
        out1 = self.layernorm1(attn1 + x)

        attn2, attn_weights_block2 = self.mha2(enc_output, enc_output, out1, padding_mask)
        attn2 = self.dropout2(attn2, training=training)
        out2 = self.layernorm2(attn2 + out1)

        ffn_output = self.ffn(out2)
        ffn_output = self.dropout3(ffn_output, training=training)
        out3 = self.layernorm3(ffn_output + out2)

        return out3, attn_weights_block1, attn_weights_block2

Full Transformer Model:

class Transformer(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, 
                 target_vocab_size, pe_input, pe_target, rate=0.1):
        super(Transformer, self).__init__()
        self.encoder = Encoder(num_layers, d_model, num_heads, dff, 
                               input_vocab_size, pe_input, rate)
        self.decoder = Decoder(num_layers, d_model, num_heads, dff, 
                               target_vocab_size, pe_target, rate)

        self.final_layer = tf.keras.layers.Dense(target_vocab_size)

    def call(self, inp, tar, training, enc_padding_mask, 
             look_ahead_mask, dec_padding_mask):
        enc_output = self.encoder(inp, training, enc_padding_mask)
        dec_output, attention_weights = self.decoder(
            tar, enc_output, training, look_ahead_mask, dec_padding_mask)

        final_output = self.final_layer(dec_output)

Training the model:

With the Transformer model assembled, it’s time to train it. This process is like teaching the orchestra to play a symphony, where the symphony is the task you want your model to perform (e.g., language translation, text generation).

• Training Loop

for epoch in range(epochs):
    # Initialize the training step
    for (batch, (inp, tar)) in enumerate(dataset):
        # Training code here

Conclusion

Creating a language model from scratch involves complex yet fulfilling work. Utilizing TensorFlow and Keras to implement and extend the Transformer architecture, alongside leveraging transfer learning from Hugging Face, allows for developing a robust NLP tool that reflects your distinct approach to language comprehension. Remember, NLP is a dynamic field with ongoing advancements — keep exploring and learning. Happy modeling!