LLM Foundations
Deep-dive into transformer architecture, tokenization, and model behavior through interactive visualizations and hands-on experiments.
What Are Large Language Models?
Before we dive into the technical details, let's understand what makes LLMs so powerful and how they actually "think."
š§ Think of LLMs Like Your Brain
Just like how you process language, LLMs break down text into smaller pieces, understand relationships between words, and predict what comes next. But instead of neurons, they use mathematical operations called "transformers."
š¢ Everything is Numbers
LLMs can't actually read text like you do. They convert every word into numbers (called tokens), process those numbers through billions of calculations, then convert the results back into text you can understand.
šÆ Attention is Key
The most important innovation in LLMs is "attention" - the ability to focus on relevant parts of the input when generating each new word. This is what makes them so good at understanding context and meaning.
Neural Networks: The Foundation of LLMs
Understanding neural networks is crucial to understanding how LLMs work. Let's break down this fundamental concept in simple terms.
š What Are Neural Networks?
Imagine your brain has billions of nerve cells (neurons) that connect to each other and pass messages. When you see a cat, different neurons fire and work together to recognize "this is a cat."
Artificial neural networks work similarly! They have artificial "neurons" (just math operations) that connect to each other. When you show the network text, these artificial neurons work together to understand patterns and meaning.
š„ Input Layer
Takes in your text (converted to numbers) and passes it to the next layer. Like your eyes seeing words on a page.
š§ Hidden Layers
Multiple layers that learn patterns, grammar, facts, and relationships. GPT-4 has 96 of these layers working together!
š¤ Output Layer
Predicts what word should come next based on all the processing from previous layers. Like your brain choosing words to speak.
How LLMs Learn: The Training Process
Ever wondered how an LLM becomes so smart? It's like teaching a student, but with the entire internet as the textbook!
š The Learning Process
Training an LLM is like teaching someone to become a master of language by reading millions of books, articles, and websites. But instead of reading word by word, the AI learns by playing a massive "fill in the blank" game.
š Step 1: Data Collection
Gather millions of web pages, books, articles, and documents
ā” Step 2: Training
AI learns patterns by predicting next words millions of times
šÆ Step 3: Fine-tuning
Additional training to make responses helpful and safe
ā” Mind-Blowing Training Facts
- Data Scale: GPT-4 was trained on about 300 billion words - roughly 570,000 novels!
- Compute Power: Training cost millions of dollars in electricity and computer time
- Time: Training takes months using thousands of powerful GPUs working 24/7
- Parameters: GPT-4 has over 1 trillion parameters (adjustable settings) to learn from
The Transformer Revolution
Transformers completely changed AI. Let's understand why this architecture is so powerful and revolutionary.
š Why Transformers Changed Everything
Before transformers, AI could only read text word by word, like reading a book with a tiny window that only shows one word at a time. Transformers can see the ENTIRE text at once and understand how every word relates to every other word!
ā” Parallel Processing
Unlike older models that read sequentially (word ā word ā word), transformers can process all words simultaneously. This makes training much faster!
Transformer: [The, cat, sat] ā All at once!
šÆ Multi-Head Attention
Instead of just one attention mechanism, transformers have multiple "heads" that each focus on different types of relationships (grammar, meaning, context).
Head 2: Subject-verb links
Head 3: Semantic meaning
š Self-Attention Magic
Each word can "attend to" every other word in the sequence, creating a rich understanding of context and relationships that older models couldn't achieve.
"loves" attends to ā "She", "teaching"
"teaching" attends to ā "loves"
Scale and Emergent Abilities
As LLMs get bigger, they develop unexpected abilities that smaller models don't have. This is one of the most exciting frontiers in AI!
š The Magic of Scale
Something incredible happens when you make language models bigger and train them on more data: they suddenly develop abilities you never explicitly taught them! This is called "emergence."
š¢ Small Models
~125M parameters
Basic text completion, simple patterns
š§ Medium Models
~1.5B parameters
Better coherence, some reasoning, basic conversations
š Large Models
~175B+ parameters
Complex reasoning, creativity, specialized knowledge
ā” Frontier Models
~1T+ parameters
Human-level performance in many domains
Language Understanding vs Generation
LLMs don't just generate text - they need to deeply understand it first. Let's explore how these two abilities work together.
š Understanding First, Generation Second
Think about how you have a conversation. First, you listen and understand what the other person is saying. Then, you think about what to say back and generate your response. LLMs work in a similar way!
But here's the fascinating part: LLMs actually do BOTH at the same time. They understand the input while simultaneously preparing to generate the output. It's like understanding a question while already forming your answer!
Understanding Process
- Tokenization: Break into word pieces
- Context Analysis: Identify it's a question about weather
- Entity Recognition: "Paris" is a location
- Intent Understanding: User wants weather information
Generation Process
- Response Planning: Decide on helpful approach
- Word Selection: Choose appropriate vocabulary
- Grammar Application: Follow language rules
- Coherence Check: Ensure logical flow
š The Dual Nature of LLMs
š Reading Comprehension
Can analyze text, extract meaning, answer questions about content
āļø Creative Writing
Can generate stories, poems, essays with original ideas and style
š¬ Conversation
Can maintain context across multiple exchanges, remember what was said
š§® Problem Solving
Can break down complex problems and generate step-by-step solutions
Lab 1: Self-Attention Visualizer
What you'll learn: How transformers decide which words to "pay attention to" when processing text.
š¤ Why Does Attention Matter?
Imagine you're reading the sentence: "The cat that was sitting on the mat was very fluffy."
When you read "fluffy," your brain automatically knows it's describing the cat, not the mat. You "pay attention" to the connection between "cat" and "fluffy" even though they're separated by other words.
That's exactly what self-attention does! It helps the model understand these relationships and connections between words, no matter how far apart they are in the sentence.
Try this: Click on any cell in the attention matrix below to see how much attention that token pair gets!
Lab 2: Tokenization Playground
What you'll learn: How LLMs break down your text into "tokens" - the basic building blocks they can understand.
š§© Why Can't LLMs Just Read Words?
Imagine you're learning a new language. At first, you might only know individual words, but you don't know every possible word that exists. What do you do when you encounter a word you've never seen?
LLMs face the same problem! Instead of trying to memorize every possible word (impossible!), they break words into smaller pieces called "subwords" or "tokens." This way, they can understand new words by recognizing familiar parts.
Try different types of text: long words, numbers, punctuation, emoji, or even other languages!
Lab 3: Embedding Space Visualizer
What you'll learn: How words become numbers and why "king - man + woman = queen" actually works in math!
š Welcome to Vector Space!
Imagine you could place every word in the universe on a massive map where similar words are close together. Words like "cat" and "dog" would be neighbors, while "cat" and "spaceship" would be far apart.
That's exactly what embeddings do! They turn each word into a list of numbers (a vector) that represents its "location" in this meaning-space. The closer two vectors are, the more similar their meanings.
cat, dog, bird, fish
car, truck, bike, plane
house, school, store, office
Lab 4: Sampling Parameters Lab
What you'll learn: How to control creativity vs. accuracy when LLMs generate text - it's like tuning a creative writing assistant!
šØ The Creativity vs. Accuracy Dilemma
When an LLM generates text, it doesn't just pick the most likely next word every time. That would be boring! Instead, it uses different strategies to balance between being accurate and being creative.
Think of it like this: if you're writing a story, do you always choose the most obvious next word? Sometimes you want to surprise your reader with something unexpected! LLMs can do the same thing.
š”ļø Temperature
Like a thermostat for creativity! Low temperature = safe, predictable text. High temperature = wild, creative text (but might be nonsense).
High temp: "The cat danced mysteriously across moonbeams!"
š Top-K Sampling
Only consider the K most likely next words. Like asking "what are the top 10 words that could come next?" and ignoring everything else.
K=50: More variety, creative possibilities
šÆ Top-P (Nucleus)
Instead of a fixed number of words, this looks at the probability. "Give me enough top words to reach 90% certainty."
P=0.9: Balanced creativity and coherence
š Real-World Applications
š Creative Writing
High temperature + Top-P for imaginative stories
š Business Reports
Low temperature + Low Top-K for accuracy
š¬ Chatbots
Medium settings for helpful but engaging responses
š Code Generation
Very low temperature for correct syntax
š Key Takeaways
Let's summarize what you've learned about how LLMs actually work under the hood.
Attention is Everything
Self-attention helps models understand relationships between words, just like how your brain connects "fluffy" with "cat" even when they're far apart in a sentence.
Tokens are Building Blocks
LLMs break text into tokens (subword pieces) to handle any text efficiently. This is why they can understand new words by recognizing familiar parts.
Words Become Vectors
Embeddings turn words into numbers that capture meaning. Similar words have similar numbers, which is why math like "king - man + woman = queen" actually works!
Control the Creativity
Sampling parameters let you tune the balance between accuracy and creativity. Lower settings for facts, higher settings for creative writing!
Unit 2 Progress
Complete all interactive labs to unlock Unit 3: Prompt Engineering
1 of 4 labs completed (25%)