LLM Training Simplified: Building Your First Language Model – 1

Ever wondered how ChatGPT learned to chat? Or how language models understand and generate human-like text? Welcome to the fascinating world of LLM training — where mathematics meets linguistics, and artificial intelligence comes to life. Think of training an LLM like teaching a child language, but instead of years of natural learning, we’re doing it through carefully crafted mathematical operations and massive amounts of data.

• LLM Training – 4 Part Series
  • LLM Training Simplified: Building Your First Language Model – 1 – We are here
• LLM Training Simplified: Building Your First Language Model – 2
• LLM Training Simplified: Building Your First Language Model – 3
• LLM Training Simplified: Building Your First Language Model – 4

Before we dive in, you might find it helpful to explore some foundational resources:

• The Transformer Architecture paper – The groundbreaking research that started it all
• Andrej Karpathy’s excellent tutorial – A beginner-friendly introduction to transformers

The Evolution of Language Models: From Simple to Sophisticated

Remember when “smart” text prediction meant your phone suggesting the next word? We’ve come a long way from those simple n-gram models. Traditional language processing was like teaching a parrot – it could repeat patterns but lacked true understanding. Here’s why we needed something better:

• Traditional models could only grasp context within a few words
• Language models couldn’t understand relationships between distant words
• They struggled with ambiguity and nuance
• Their ability was limited to patterns seen in training data

Welcome to an exciting journey of LLM training, where we’ll demystify the magic behind Large Language Models by building our miniature version from scratch!

The Magic Behind Language Models: A Peek Under the Hood

Imagine teaching a child to complete sentences. You might start with simple phrases like “The sky is _” and watch them learn to say “blue.” Now picture doing this at a massive scale, with billions of examples, and you’ve got the basic idea behind how language models learn. But instead of human neurons making these connections, we use artificial ones, carefully crafted through mathematical operations and training data.

Meet Mini-GPT: Our Friendly Language Model

In this series, we’re building Mini-GPT — a smaller but fully functional language model that will help us understand the core principles behind its bigger cousins like GPT-3 and ChatGPT. Think of it as building a model aeroplane: while it won’t cross the Atlantic, it teaches us everything about how planes fly!

What Makes Mini-GPT Special?

Our Mini-GPT will feature:

• A vocabulary of 5,000 words (compared to GPT-3’s 50,000+)
• 4 layers of transformer blocks (versus 96 in GPT-3)
• Ability to handle sequences of up to 128 tokens (GPT-3 manages 2048)
• Training on a focused dataset of ~100MB of text

While these numbers might seem small compared to industrial-scale models, they’re perfect for learning and experimentation. Plus, you can train this on your CPU hardware!

The Building Blocks: Architecture Overview

Let’s break down the key components we’ll need during LLM training and implementation:

1. The Tokenizer: Words into Numbers 🔢

Just as our brains convert written words into neural signals, our model needs to convert text into numbers. Our tokenizer will be the gateway between human language and machine understanding.

# Preview of what we'll build
text = "Hello, world!"
tokens = tokenizer.encode(text)
# [15496, 2961, 0] # Each word becomes a number

This code might look simple, but it’s performing a crucial transformation. The tokenizer converts human-readable text into numerical tokens that our model can process. Without this step, our neural network would have no way to interpret language. Each number uniquely identifies a specific word or subword in our vocabulary, allowing the model to work with discrete tokens rather than arbitrary strings.

To learn more on vectorisation refer our guide on Vectorization Simplified for Training LLMs

2. The Attention Mechanism: Understanding Context 🔍

This is where the magic happens! Attention allows our model to understand relationships between words, just like how we understand that in “The cat sat on the mat because it was comfortable”, “it” refers to “mat”.

The attention mechanism is revolutionary because it lets each word “look at” all other words in the sentence to determine relevance. When we implement this, we’ll create a mechanism that calculates how much each word should “pay attention” to every other word, giving our model a way to capture long-range dependencies that previous architectures struggled with.

3. Position Embeddings: Order Matters 📝

Words don’t exist in isolation – their order matters! Position embeddings help our model understand that “Dog bites man” and “Man bites dog” mean very different things.

Unlike traditional neural networks that treat inputs as unordered sets, our position embeddings will encode the sequential nature of language. This is vital because the meaning of text depends heavily on word order. The embedding we’ll build uses mathematical patterns to create unique signatures for each position, allowing the model to understand sequence without sacrificing the benefits of parallel processing.

4. The Transformer Block: Putting It All Together 🏗️

Think of this as the brain of our model, where all the components work together to process and understand text.

The transformer block combines multiple mechanisms: self-attention to capture relationships, feed-forward networks to process information, and normalization layers to stabilize training. When we implement this, we’ll see how these pieces fit together to create a powerful text-processing unit that can be stacked to form deeper networks.

What Can Mini-GPT Do?

By the end of our journey, our Mini-GPT will be able to:

• Complete sentences and generate coherent text
• Understand basic context and relationships between words
• Demonstrate understanding of simple patterns and structures in language

Here’s a sneak peek at what we’ll achieve:

prompt = "The artificial intelligence"
generated = minigpt.generate(prompt)
print(generated)
# Output: "The artificial intelligence revolution has transformed how we approach problem-solving in the modern world."

The beauty of this simple interface hides the complex processing happening behind the scenes. When you run this code, the model will take each word, process it through multiple transformer layers, and predict the most likely next word based on patterns it learned during training. It repeats this process, using each new word to update its context understanding until it produces a complete thought.

The Road Ahead

In the upcoming parts, we’ll:

1. Build each component from scratch with clear, commented code
2. Train our model on real text data
3. Generate our AI-created content
4. Understand the challenges and solutions in LLM training

Why This Matters

Understanding how language models work isn’t just academic curiosity. As AI continues to reshape our world, knowing the fundamentals of these systems becomes increasingly valuable. Whether you’re a developer, researcher, or just AI-curious, building your own Mini-GPT will give you invaluable insights into the technology that’s changing our world.

Ready to begin this exciting journey? In the next part, we’ll dive into setting up our development environment and building our first components!

Remember: The goal of this LLM training isn’t to compete with ChatGPT, but to understand the fundamental principles that make all language models work. Let’s start building! 🚀

Next up in LLM Training Part 2: “Laying the Foundation – Building Mini-GPT’s Brain” where we’ll write our first lines of code and bring our model to life!

Want to explore more? Check out these additional resources:

• Stanford’s small language models research
• HuggingFace’s guide to efficient training