Chapter 6: The Complete Roadmap - Building LLMs in 3 Stages
📖 Reading Time: 35 minutes
Welcome to Chapter 6! This is where we plan our complete journey for building LLMs from scratch! 🗺️
What makes this series different?
Most tutorials skip critical details. We’re going deep into every single stage - from data preparation to deploying production-ready applications.
Today’s goal:
Give you a crystal-clear roadmap of the entire journey ahead so you know exactly what to expect!
Let’s begin! 🚀
📑 Table of Contents
- Why This Roadmap Matters
- Quick Recap: What We’ve Learned So Far
- The 3-Stage Journey
- Stage 1: Building Blocks
- Stage 2: Pre-Training
- Stage 3: Fine-Tuning
- Why Every Stage Matters
- What Most People Get Wrong
- Key Takeaways from Chapters 1-5
- What’s Next
Why This Roadmap Matters
🎯 The Problem
Most LLM tutorials on YouTube:
❌ Skip Stage 1 (building blocks)
❌ Skip Stage 2 (pre-training)
❌ Only cover Stage 3 (fine-tuning with tools)
Result: You can use tools, but don’t understand what’s happening under the hood.
✅ Our Approach
This series covers ALL 3 stages in detail:
✅ Stage 1: Data preparation, tokenization, attention mechanisms, architecture
✅ Stage 2: Pre-training loops, model evaluation, weight loading
✅ Stage 3: Fine-tuning for real applications
Goal: Make you confident about the nuts and bolts of LLMs!
💡 Who This Series Is For
Perfect if you are:
- 🎓 Students wanting deep understanding
- 💼 Working professionals building LLM applications
- 🚀 Startup founders needing production-ready systems
- 📊 Managers wanting technical depth
- 🧪 Researchers exploring LLM internals
By the end: You’ll understand LLMs from theory to production!
Quick Recap: What We’ve Learned So Far
📚 Chapters 1-5 Summary
| Chapter | Topic | Key Learnings |
|---|---|---|
| Chapter 1 | Series Introduction | Overview, prerequisites, what to expect |
| Chapter 2 | What are LLMs? | Basics, how they work, real examples |
| Chapter 3 | Pre-training vs Fine-tuning | Two-stage training process, costs, examples |
| Chapter 4 | Transformer Architecture | Encoder-decoder, self-attention, BERT vs GPT |
| Chapter 5 | GPT Architecture | Evolution, decoder-only, 175B parameters, emergent behavior |
We’ve built the foundation! Now let’s map the journey ahead.
The 3-Stage Journey
🗺️ The Big Picture
┌─────────────────────────────────────────────────┐
│ BUILDING LLMs FROM SCRATCH │
├─────────────────────────────────────────────────┤
│ │
│ STAGE 1: Building Blocks │
│ ├── Data Preparation & Sampling │
│ ├── Attention Mechanisms │
│ └── LLM Architecture │
│ │
│ ↓ │
│ │
│ STAGE 2: Pre-Training │
│ ├── Training Loop │
│ ├── Model Evaluation │
│ └── Loading Pre-trained Weights │
│ │
│ ↓ │
│ │
│ STAGE 3: Fine-Tuning │
│ ├── Spam Classification App │
│ └── Personal Chatbot App │
│ │
└─────────────────────────────────────────────────┘
Each stage builds on the previous one!
⏱️ Timeline
Complete series: 2-3 months
Stage 1: ~6-8 weeks (most detailed!)
Stage 2: ~3-4 weeks
Stage 3: ~2-3 weeks
Total lectures: 40-50 (we’re at Chapter 6 now!)
📖 Our Guide
This series heavily follows:
Book: “Building a Large Language Model from Scratch”
Author: Sebastian Raschka
Why this book?
- Extremely detailed
- Code-first approach
- Production-ready practices
- Battle-tested concepts
Stage 1: Building Blocks
🏗️ Goal: Understand How LLMs Work
Stage 1 is the LONGEST and MOST IMPORTANT!
We’ll spend the most time here because this is where you build deep understanding.
📦 What We’ll Cover in Stage 1
Stage 1
├── Data Preparation & Sampling
│ ├── Tokenization
│ ├── Vector Embeddings
│ ├── Positional Encoding
│ └── Data Batching
│
├── Attention Mechanisms
│ ├── Self-Attention from scratch
│ ├── Multi-Head Attention
│ ├── Masked Attention
│ └── Key-Query-Value concepts
│
└── LLM Architecture
├── Stacking layers
├── Feed-forward networks
├── Layer normalization
└── Building complete GPT architecture
1️⃣ Data Preparation & Sampling
A. Tokenization
What is it?
Breaking sentences into smaller units (tokens).
Example:
Sentence: "The cat sat on the mat"
Tokens: ["The", "cat", "sat", "on", "the", "mat"]
But it’s more complex than just splitting by spaces!
Different tokenization methods:
- Word-level tokenization
- Character-level tokenization
- Subword tokenization (BPE, WordPiece)
We’ll implement:
- Byte Pair Encoding (BPE)
- Building custom tokenizers
- Handling special characters
- Vocabulary creation
B. Vector Embeddings
The Problem:
Computers can’t understand words like “apple” or “king”.
The Solution:
Convert words into numbers (vectors)!
Example:
"apple" → [0.2, 0.8, 0.1, 0.5, 0.3] (512-dimensional vector)
"banana" → [0.3, 0.7, 0.2, 0.6, 0.2]
"king" → [0.9, 0.1, 0.8, 0.2, 0.7]
Key Insight:
Similar words should have similar vectors!
┌─────────────────────────────────────────────┐
│ VECTOR EMBEDDING SPACE │
├─────────────────────────────────────────────┤
│ │
│ 🟢 Fruits: │
│ apple ● │
│ banana ● orange ● │
│ │
│ 🔵 Royalty: │
│ king ● queen ● │
│ man ● woman ● │
│ │
│ 🟡 Sports: │
│ football ● │
│ tennis ● golf ● │
│ │
└─────────────────────────────────────────────┘
Why this matters:
Words with similar meanings cluster together in vector space!
What we’ll learn:
- How embeddings capture semantic meaning
- Word2Vec, GloVe concepts
- Building embedding layers
- Visualizing embeddings
C. Positional Encoding
The Problem:
“Dog bites man” ≠ “Man bites dog”
Order matters!
The Solution:
Add positional information to each word.
How it works:
Original: "The cat sat"
Tokens: ["The", "cat", "sat"]
After embedding:
"The" → [0.2, 0.8, ...]
"cat" → [0.5, 0.3, ...]
"sat" → [0.7, 0.1, ...]
After positional encoding:
"The" at position 0 → [0.2 + pos_0, 0.8 + pos_0, ...]
"cat" at position 1 → [0.5 + pos_1, 0.3 + pos_1, ...]
"sat" at position 2 → [0.7 + pos_2, 0.1 + pos_2, ...]
Now the model knows the order!
What we’ll learn:
- Sinusoidal positional encoding
- Learnable positional embeddings
- Why position matters
- Implementation from scratch
D. Data Batching & Context Windows
The Challenge:
Training on billions of tokens is SLOW!
The Solution:
Feed data in batches!
Example:
Full dataset:
300 billion tokens (GPT-3 scale)
Break into batches:
Batch 1: 1024 tokens
Batch 2: 1024 tokens
Batch 3: 1024 tokens
...
Batch N: 1024 tokens
Context Window:
How many previous words should the model see?
Example:
Context = 4 words
Training examples from: "The cat sat on the mat"
Input: "The cat sat on" → Target: "the"
Input: "cat sat on the" → Target: "mat"
What we’ll learn:
- Creating training batches
- Context window sizes
- Efficient data loading
- Next-word prediction setup
- Memory optimization
2️⃣ Attention Mechanisms
This is the HEART of LLMs! ❤️
What is Attention?
Attention allows the model to focus on relevant words when predicting the next word.
Example:
Sentence: “The cat sat on the mat because it was tired”
Task: What does “it” refer to?
Without attention:
Model: Confused! "it" could mean cat or mat?
With attention:
Model: "it" pays attention to "cat" (not "mat")
Because: "tired" is something a cat feels, not a mat!
Attention weights:
"it" → "cat": 0.8 (high attention!)
"it" → "mat": 0.1 (low attention)
"it" → "tired": 0.1
Key Components We’ll Build
A. Self-Attention
- How a word relates to other words
- Computing attention scores
- Implementing from scratch in Python
B. Key-Query-Value (K-Q-V)
Query: What am I looking for?
Key: What do I contain?
Value: What information do I provide?
Think of it like a search engine:
You type: "best pizza near me" ← Query
Websites have: keywords (titles) ← Keys
Websites contain: actual content ← Values
Attention finds best match between your query and keys,
then returns the corresponding values!
C. Multi-Head Attention
Instead of one attention mechanism, use MULTIPLE!
Why?
- Head 1: Focuses on grammar
- Head 2: Focuses on entities (names, places)
- Head 3: Focuses on sentiment
- Head 4: Focuses on relationships
More heads = Richer understanding!
GPT-3 has 96 attention heads per layer!
D. Masked Attention
For GPT (decoder-only):
Can’t see future words! (Otherwise it’s cheating!)
Example:
Sentence: "The cat sat on the mat"
When predicting "sat":
Can see: "The", "cat" ✅
Can't see: "on", "the", "mat" ❌ (masked!)
What we’ll implement:
- Complete self-attention from scratch
- K-Q-V calculations
- Multi-head attention
- Attention masks
- Visualizing attention weights
3️⃣ LLM Architecture
Finally, we ASSEMBLE everything!
Building the Complete GPT Model
┌─────────────────────────────────────────┐
│ GPT ARCHITECTURE │
├─────────────────────────────────────────┤
│ │
│ Input Text │
│ ↓ │
│ [Tokenization] │
│ ↓ │
│ [Token Embeddings] │
│ ↓ │
│ [Positional Encoding] │
│ ↓ │
│ ┌─────────────────┐ │
│ │ Decoder Block 1 │ │
│ │ - Multi-Head │ │
│ │ Attention │ │
│ │ - Feed Forward │ │
│ │ - Layer Norm │ │
│ └─────────────────┘ │
│ ↓ │
│ ┌─────────────────┐ │
│ │ Decoder Block 2 │ │
│ └─────────────────┘ │
│ ↓ │
│ ... (96 blocks for GPT-3!) │
│ ↓ │
│ [Output Layer] │
│ ↓ │
│ Predicted Next Word │
│ │
└─────────────────────────────────────────┘
What we’ll build:
✅ Complete decoder block
✅ Layer normalization
✅ Residual connections
✅ Feed-forward networks
✅ Stacking multiple layers
✅ Final output projection
✅ Full GPT architecture in PyTorch/TensorFlow
🎯 Stage 1 Outcome
By the end of Stage 1, you’ll have:
✅ Complete understanding of tokenization
✅ Implemented vector embeddings from scratch
✅ Built positional encoding
✅ Created efficient data batching
✅ Implemented self-attention mechanism
✅ Built multi-head attention
✅ Assembled complete GPT architecture
✅ Ready-to-train LLM model!
Estimated time: 6-8 weeks (20-25 lectures)
Stage 2: Pre-Training
🚀 Goal: Train Your LLM on Unlabeled Data
Once we have the architecture ready, we train it!
📦 What We’ll Cover in Stage 2
Stage 2: Pre-Training
├── Training Loop
│ ├── Forward pass
│ ├── Loss calculation
│ ├── Backward pass (gradients)
│ └── Parameter updates
│
├── Model Evaluation
│ ├── Training vs validation loss
│ ├── Text generation quality
│ ├── Perplexity metrics
│ └── Monitoring training progress
│
└── Loading Pre-trained Weights
├── Saving model checkpoints
├── Loading saved models
└── Using OpenAI pre-trained weights
1️⃣ The Training Loop
This is where the magic happens!
What Happens During Training?
For each epoch:
For each batch of data:
1. Forward pass (get predictions)
2. Calculate loss (how wrong are we?)
3. Backward pass (calculate gradients)
4. Update parameters (improve the model)
5. Repeat!
Visual Example:
Epoch 1:
Batch 1: Loss = 5.2
Batch 2: Loss = 5.0
Batch 3: Loss = 4.8
...
Epoch 2:
Batch 1: Loss = 4.5
Batch 2: Loss = 4.3
...
Epoch 10:
Batch 1: Loss = 1.2 ← Much better!
...
Lower loss = Better model!
Next-Word Prediction Training
Example:
Sentence: “The cat sat on the mat”
Training examples:
| Input | Target | Prediction | Loss |
|---|---|---|---|
| “The” | “cat” | “dog” ❌ | High loss |
| “The cat” | “sat” | “jumped” ❌ | High loss |
| “The cat sat” | “on” | “under” ❌ | High loss |
After training:
| Input | Target | Prediction | Loss |
|---|---|---|---|
| “The” | “cat” | “cat” ✅ | Low loss |
| “The cat” | “sat” | “sat” ✅ | Low loss |
| “The cat sat” | “on” | “on” ✅ | Low loss |
The model learns to predict correctly!
What We’ll Implement
A. Training Function
def train_llm(model, data, epochs, lr):
for epoch in range(epochs):
for batch in data:
# Forward pass
predictions = model(batch.input)
# Calculate loss
loss = calculate_loss(predictions, batch.target)
# Backward pass
gradients = calculate_gradients(loss)
# Update weights
update_parameters(gradients, lr)
return trained_model
B. Loss Functions
- Cross-entropy loss
- Why it works for language modeling
- Interpreting loss values
C. Optimization
- Adam optimizer
- Learning rate scheduling
- Gradient clipping
- Mixed precision training
2️⃣ Model Evaluation
How do we know if training is working?
A. Training vs Validation Loss
Split data:
Total data: 100%
├── Training: 90% (used for training)
└── Validation: 10% (used for evaluation)
Track both losses:
Epoch 1:
Training loss: 5.2
Validation loss: 5.3
Epoch 10:
Training loss: 1.2
Validation loss: 1.5
Epoch 50:
Training loss: 0.3
Validation loss: 2.0 ← Overfitting!
If validation loss increases while training loss decreases = OVERFITTING!
B. Text Generation Evaluation
Test the model by generating text!
Epoch 1:
Input: "Once upon a time"
Output: "asdf qwer zxcv poiu" ← Garbage!
Epoch 10:
Input: "Once upon a time"
Output: "there there there there" ← Repetitive
Epoch 50:
Input: "Once upon a time"
Output: "there was a princess who lived in a castle" ← Good!
Visual inspection is important!
C. Metrics We’ll Track
1. Perplexity
- Measures how “surprised” the model is
- Lower perplexity = Better model
- GPT-3 perplexity: ~20
2. Loss Curves
- Plot training and validation loss
- Identify overfitting
- Determine when to stop training
3. Generation Quality
- Coherence
- Grammar
- Factual accuracy
- Creativity
3️⃣ Saving & Loading Models
Training takes WEEKS! We can’t start from scratch every time!
A. Saving Checkpoints
Save model every N epochs:
Epoch 10: Save → model_epoch_10.pt
Epoch 20: Save → model_epoch_20.pt
Epoch 30: Save → model_epoch_30.pt
If training crashes, we can resume!
B. Loading Pre-trained Weights
OpenAI has released pre-trained GPT-2 weights!
We’ll learn to:
- Download OpenAI weights
- Load them into our model
- Fine-tune from pre-trained weights
- Save compute time and money!
Example:
# Instead of training from scratch (30 days):
model = train_from_scratch() # 30 days, $4.6M
# Load pre-trained and fine-tune (3 hours):
model = load_pretrained_weights("gpt2")
model = finetune(model, my_data) # 3 hours, $100
Huge savings! 💰
🎯 Stage 2 Outcome
By the end of Stage 2, you’ll have:
✅ Implemented complete training loop
✅ Trained your own small LLM
✅ Evaluated model performance
✅ Saved and loaded model checkpoints
✅ Loaded OpenAI pre-trained weights
✅ Built a foundational model!
Estimated time: 3-4 weeks (10-12 lectures)
Stage 3: Fine-Tuning
🎯 Goal: Build Production-Ready Applications
Pre-trained models are generic. Fine-tuning makes them specific and useful!
📦 What We’ll Cover in Stage 3
Stage 3: Fine-Tuning
├── Application 1: Spam Classifier
│ ├── Collecting labeled data
│ ├── Fine-tuning on spam/not-spam
│ └── Deploying classifier
│
└── Application 2: Personal Chatbot
├── Instruction-input-output format
├── Fine-tuning for Q&A
└── Building chat interface
🚀 Application 1: Email Spam Classifier
The Problem
You receive thousands of emails daily. Which are spam?
The Dataset
Labeled emails:
| Label | |
|---|---|
| “Congratulations! You won $1000!” | SPAM ✅ |
| “Hey, are we still on for dinner?” | NOT SPAM ✅ |
| “Click here for FREE iPhone!” | SPAM ✅ |
| “Meeting at 3 PM tomorrow” | NOT SPAM ✅ |
1000s of labeled examples!
The Process
Step 1: Load Pre-trained Model
model = load_pretrained_gpt2()
Step 2: Fine-tune on Spam Data
model = finetune(model, spam_dataset, epochs=5)
Step 3: Test
email = "You are a winner! Claim your prize now!"
prediction = model.classify(email)
# Output: "SPAM" ✅
What We’ll Build
✅ Data collection pipeline
✅ Data preprocessing
✅ Fine-tuning script
✅ Evaluation metrics (accuracy, F1-score)
✅ Production deployment
✅ Working spam classifier!
💬 Application 2: Personal Assistant Chatbot
The Goal
Build your own ChatGPT-like assistant!
The Dataset Format
Instruction-Input-Output format:
{
"instruction": "Translate English to French",
"input": "Hello, how are you?",
"output": "Bonjour, comment allez-vous?"
}
{
"instruction": "Summarize the text",
"input": "Long article about AI...",
"output": "Brief summary..."
}
{
"instruction": "Answer the question",
"input": "What is the capital of France?",
"output": "Paris"
}
1000s of instruction-following examples!
The Process
Step 1: Prepare Instruction Dataset
- Collect Q&A pairs
- Format in instruction-input-output style
- Clean and validate
Step 2: Fine-tune
model = load_pretrained_gpt2()
model = finetune_instruct(model, instruction_dataset)
Step 3: Chat Interface
You: What's the weather like?
Bot: I'm sorry, I don't have access to real-time weather data.
You: Explain quantum computing simply.
Bot: Quantum computing uses quantum bits (qubits) which can be
in multiple states simultaneously...
What We’ll Build
✅ Instruction dataset preparation
✅ Fine-tuning for instruction-following
✅ Chat interface (CLI or web)
✅ Context management (multi-turn conversations)
✅ Response quality evaluation
✅ Your own personal AI assistant!
🎯 Stage 3 Outcome
By the end of Stage 3, you’ll have:
✅ Built spam email classifier
✅ Created personal chatbot
✅ Learned production fine-tuning
✅ Deployed real applications
✅ Production-ready LLM skills!
Estimated time: 2-3 weeks (8-10 lectures)
Why Every Stage Matters
⚠️ The Problem with Skipping Stages
Many people do this:
❌ Skip Stage 1 → Don't understand architecture
❌ Skip Stage 2 → Don't understand training
✅ Only Stage 3 → Use LangChain/tools
Result: Can use tools, but NO deep understanding
This leaves you:
- 😰 Insecure about your knowledge
- 🤷 Unable to debug issues
- 🚫 Can’t customize beyond tutorials
- ❌ Not production-ready
✅ The Right Approach
Complete all 3 stages:
✅ Stage 1: Deep understanding of architecture
✅ Stage 2: Know how training works
✅ Stage 3: Build real applications
Result: Confident LLM engineer!
This makes you:
- 💪 Confident in your skills
- 🐛 Able to debug complex issues
- 🔧 Can customize for your needs
- 🚀 Production-ready engineer
🎯 Our Philosophy
Theory + Practice = Mastery
We’ll do both:
- 📝 Whiteboard explanations (theory)
- 💻 Jupyter notebooks (code)
- 🧪 Hands-on experiments
- 🏗️ Real projects
You’ll understand the “why” AND the “how”!
What Most People Get Wrong
❌ Mistake 1: Using Only Tools
Common approach:
from langchain import LLM
model = LLM("gpt-3.5-turbo")
response = model("Tell me a joke")
Problem: Works great until it doesn’t! Then you’re stuck.
❌ Mistake 2: Skipping Fundamentals
Jump straight to:
- Fine-tuning tutorials
- LangChain
- Vector databases
- RAG systems
Without understanding:
- How tokenization works
- What attention does
- Why pre-training matters
- How gradients flow
Result: Shallow knowledge
❌ Mistake 3: Only Pre-training OR Only Fine-tuning
Some people:
- Study pre-training theory forever
- Never deploy anything
Others:
- Fine-tune models blindly
- Don’t understand what’s happening
Both are wrong!
✅ The Right Way
Complete journey:
- Understand fundamentals (Stage 1)
- Learn training process (Stage 2)
- Build applications (Stage 3)
Then you’re a COMPLETE LLM engineer! 🚀
Key Takeaways from Chapters 1-5
Let’s recap what we’ve learned so far:
1️⃣ LLMs Have Transformed NLP
Before LLMs:
- Separate model for each task
- Classification → Model A
- Translation → Model B
- Summarization → Model C
With LLMs:
- One model for ALL tasks!
- Pre-trained once, used for everything
- Emergent properties unlock new abilities
Game changer! 🎯
2️⃣ Two-Stage Training Process
Stage 1: PRE-TRAINING
├── Unlabeled data (billions of words)
├── Task: Predict next word
├── Cost: $4.6 million (GPT-3)
└── Output: Foundational model
↓
Stage 2: FINE-TUNING
├── Labeled data (thousands of examples)
├── Task: Specific application
├── Cost: $100 - $10,000
└── Output: Production-ready model
Fine-tuned models ALWAYS outperform pre-trained only models on specific tasks!
3️⃣ Transformer Architecture is the Secret Sauce
Key innovation: Self-attention mechanism
What it does:
- Gives LLM access to entire context
- Understands word importance
- Not just current sentence, but previous ones too!
Example:
"The cat sat on the mat because it was tired"
"it" pays attention to "cat" (not "mat")
because "tired" relates to cats!
Attention = Understanding context!
4️⃣ GPT vs Transformer
Original Transformer (2017):
- Encoder + Decoder
- For translation tasks
GPT (2018+):
- Decoder ONLY
- For text generation
- Simpler, but scaled MUCH larger
GPT-3: 96 decoder layers, 175B parameters
5️⃣ Emergent Behavior
Mind-blowing discovery:
Training task: Predict next word (ONLY!)
What LLMs can also do (without training!):
- ✅ Translation
- ✅ Summarization
- ✅ Classification
- ✅ Code generation
- ✅ Question answering
- ✅ Creative writing
Nobody fully understands why! 🤯
Active research area!
6️⃣ Evolution Timeline
2017: Transformers (Google)
2018: GPT-1 (117M params)
2019: GPT-2 (1.5B params)
2020: GPT-3 (175B params) ← Game changer!
2022: ChatGPT → Goes viral!
2023: GPT-4 (~1T params?)
2024: GPT-4o (current)
7 years from research paper to ChatGPT!
7️⃣ Cost & Compute
Pre-training GPT-3:
- 💰 Cost: $4.6 million
- 🖥️ Hardware: 10,000 GPUs
- ⚡ Power: Enough for a small town
- ⏱️ Time: 30+ days
Only ~10-15 companies globally can afford this!
Good news: You can fine-tune for $100-$10,000!
8️⃣ Zero-Shot vs Few-Shot
Zero-shot: No examples needed
"Translate to French: breakfast"
→ "petit-déjeuner"
Few-shot: Provide examples
Examples:
sea otter → loutre de mer
peppermint → menthe poivrée
Translate: breakfast
→ "petit-déjeuner" (higher confidence!)
More examples = Better results!
What’s Next
🚀 Chapter 7: Working with Text Data
Next lecture starts Stage 1!
We’ll cover:
- Loading text datasets
- Character-level analysis
- Breaking text into tokens
- Vocabulary creation
- Hands-on Jupyter notebooks!
From theory to CODE! 💻
📅 Lecture Schedule
Lectures 1-6: Introduction & Theory ✅ (Done!)
Lectures 7-30: Stage 1 (Building Blocks)
- Data preparation
- Attention mechanisms
- LLM architecture
Lectures 31-42: Stage 2 (Pre-Training)
- Training loops
- Evaluation
- Weight management
Lectures 43-50: Stage 3 (Fine-Tuning)
- Spam classifier
- Personal chatbot
- Deployment
🎯 How to Get the Most from This Series
1. Watch in order
- Don’t skip lectures
- Each builds on previous
2. Run the code
- Jupyter notebooks provided
- Experiment yourself
- Break things and fix them!
3. Ask questions
- Comment on videos
- We respond to every comment!
- Learn together as a community
4. Build your own projects
- Apply concepts to your domain
- Share your work
- Get feedback
💻 What You’ll Need
Software:
- Python 3.8+
- PyTorch or TensorFlow
- Jupyter Notebook
- GPU (optional, but recommended)
Hardware:
- For learning: Any laptop works!
- For serious training: GPU (NVIDIA recommended)
- Cloud options: Google Colab, AWS, Azure
We’ll guide you through setup in next lecture!
Chapter Summary
🎓 What We Learned Today
This was a ROADMAP chapter!
The 3 Stages:
Stage 1: Building Blocks (6-8 weeks)
- Data preparation & sampling
- Attention mechanisms
- LLM architecture
- Outcome: Ready-to-train model
Stage 2: Pre-Training (3-4 weeks)
- Training loop
- Model evaluation
- Weight management
- Outcome: Foundational model
Stage 3: Fine-Tuning (2-3 weeks)
- Spam classifier
- Personal chatbot
- Outcome: Production-ready apps
Why This Approach is Different:
✅ Covers ALL 3 stages
✅ Deep dive into every concept
✅ Theory + Practice
✅ Production-ready skills
✅ No shortcuts!
Key Philosophy:
Understanding > Tools
Theory + Practice = Mastery
Build from Scratch → True Confidence
📚 Recap: Chapters 1-5
- Chapter 1: Series overview
- Chapter 2: What are LLMs?
- Chapter 3: Pre-training vs Fine-tuning
- Chapter 4: Transformer architecture
- Chapter 5: GPT evolution & architecture
- Chapter 6 (Today): Complete roadmap
🔜 Next Chapter Preview
Chapter 7: Working with Text Data
We’ll learn:
- Loading datasets
- Text analysis
- Tokenization basics
- Building vocabulary
- First Jupyter notebook!
Theory → Code starts NOW! 🚀
🚀 Take Action Now!
- ✅ Bookmark This Chapter - Your roadmap reference!
- 📝 Leave a Comment - Which stage excites you most?
- 🔔 Subscribe - Don’t miss upcoming code tutorials
- 💻 Prepare Your Environment - Install Python & Jupyter
- 🔥 Get Ready to CODE - Next chapter starts hands-on!
Quick Reference
The 3 Stages:
| Stage | Focus | Duration | Outcome |
|---|---|---|---|
| Stage 1 | Building Blocks | 6-8 weeks | Ready-to-train model |
| Stage 2 | Pre-Training | 3-4 weeks | Foundational model |
| Stage 3 | Fine-Tuning | 2-3 weeks | Production apps |
Stage 1 Topics:
- Tokenization
- Vector Embeddings
- Positional Encoding
- Data Batching
- Self-Attention
- Multi-Head Attention
- LLM Architecture
Stage 2 Topics:
- Training Loop
- Loss Calculation
- Gradient Updates
- Training vs Validation
- Model Evaluation
- Saving & Loading Weights
Stage 3 Topics:
- Spam Classification
- Personal Chatbot
- Fine-tuning Process
- Production Deployment
Thank You!
You’ve completed Chapter 6 - The Roadmap! 🗺️
You now have:
- Clear understanding of the 3-stage journey
- Knowledge of what’s coming next
- Excitement for hands-on coding
Next chapter: We start CODING! 💻
Remember:
- Every stage matters
- Theory + Practice = Mastery
- We’re building from scratch
- You’ll understand the nuts and bolts
Get ready for the most detailed LLM series on the internet! 🚀
📣 Your Feedback Matters!
This series is being built WITH you!
Comment below:
- Which stage are you most excited about?
- What specific topics do you want covered?
- Any questions about the roadmap?
We respond to EVERY comment!
🎯 Series Goal
By the end (2-3 months from now):
✅ Deep understanding of LLMs
✅ Built LLM from scratch
✅ Trained your own model
✅ Deployed real applications
✅ Confident LLM Engineer
Let’s build this together! 💪
See you in Chapter 7 where we start coding! 🚀
Questions? Comments? Feedback? Drop them below! We read and respond to every single one. 💬
