A new series by DataPeCharcha
Your 16-week journey to finally understand Large Language Models. No jargon, just clarity on how models like GPT-4 *really* work.
WEEK 1
We start at the very beginning, exploring how early models predicted text by simply counting which words appeared together most often. It's the simple, statistical foundation for everything that followed.
WEEK 2
This week, we see the first spark of true 'understanding.' Learn how we taught computers that 'dog' and 'puppy' are related, and how RNNs tried to give models a basic memory.
WEEK 3
Early neural nets were forgetful. Discover the clever 'gate' system in LSTMs that gave models a reliable long-term memory, a breakthrough that dominated NLP for years.
WEEK 4
Computers don't read words, they read numbers. We'll break down the essential process of 'tokenization'—turning text into a language machines can finally understand.
WEEK 5
Before the magic happens, we have to prepare the data. Learn how a Transformer gives each word its initial meaning and, crucially, a 'timestamp' so it knows the order of the sentence.
WEEK 6
This is the revolutionary concept that changed everything. We'll use simple analogies to explain how words 'talk' to each other to figure out the true context of a sentence.
WEEK 7
One 'conversation' between words isn't enough. See how Multi-Head Attention allows words to look at the sentence from many different perspectives at once for a richer understanding.
WEEK 8
After words gather context, they need time to 'think.' That's the job of the Feed-Forward Network, a key component that processes the information gathered by attention.
WEEK 9
Stacking these layers creates problems. Discover the simple but brilliant tricks—'shortcuts' and 'stabilizers'—that allow us to build incredibly deep and powerful models without them breaking.
WEEK 10
How does an LLM learn from the entire internet? We'll cover the simple goal of 'predicting the next word' and the core training loop that makes it all possible.
WEEK 11
A raw model is just a text predictor. Learn about Supervised Fine-Tuning (SFT), the process of training the model on examples to turn it into a helpful instruction-follower.
WEEK 12
How do we make sure models are helpful and harmless? We'll explore RLHF, the advanced technique that uses human preferences to align the model's behavior with our values.
WEEK 13
Once the model has learned, how does it choose its words? We'll look at the straightforward, predictable methods that form the basis of text generation.
WEEK 14
To sound less robotic, models need a bit of randomness. Learn about the clever sampling techniques that allow for more creative, diverse, and human-like writing.
WEEK 15
Making models bigger is expensive. Discover the Mixture of Experts (MoE) architecture, a clever 'committee of specialists' approach that allows models to scale to trillions of parameters efficiently.
WEEK 16
The journey ends by looking ahead. We'll explore how LLMs are breaking the text barrier to understand images, audio, and video, getting closer to a human-like understanding of the world.