Build Your Own LLM
The LLM Sovereignty Stack™ — Stop Renting AI, Start Owning It
This is not another course on using APIs. This is executive business education (Harvard/MIT/Stanford caliber) merged with a masterclass for tech founders and AI leaders. Using the DrLee.AI Shu-Ha-Ri learning method, you'll go from API consumer to model builder in 9 transformative steps. Each module begins with a TedTalk-style presentation, then you immediately build it yourself with hands-on coding. You'll construct a complete GPT architecture from scratch, train on real data, fine-tune for your use cases, and deploy with zero API dependency. By the end, you won't just understand how LLMs work—you'll own production-ready models that become your competitive moat. Available in 4 modalities: 9-Week Live Cohort, 5-Day Immersive Bootcamp, Self-Paced Mastery, or Founder's Edition (1:1 mentorship/Fractional CTO).
30-day money-back guarantee
What You'll Build
By the end of this masterclass, you'll have built real systems and gained deep understanding.
Build complete GPT architecture from scratch in PyTorch (4,000+ lines of code you understand)
Implement self-attention, causal attention, and multi-head attention mechanisms from first principles
Code the complete transformer: embeddings, layer normalization, feed-forward networks, residual connections
Tokenize text using BPE (same technique as modern ChatGPT/Claude/Gemini), create embeddings, encode positions
Train language models on 100M+ tokens using next-token prediction
Fine-tune for classification tasks (spam detection, sentiment analysis) with 95%+ accuracy
Fine-tune for instruction-following to create your own ChatGPT-style assistant
Implement LoRA for parameter-efficient fine-tuning (0.1% of parameters, 10x faster training)
Apply production training techniques: warmup, cosine decay, gradient clipping
Deploy production models with zero API costs—own your model weights forever
Course Curriculum
9 modules · 50 hours of content
Step 1: The Architecture of Intelligence
7 lessons
- The Nature of Language Models: From Pattern Matching to Understanding
- Real-World Applications and Possibilities: Where LLMs Create Business Value
- The Three-Stage Journey: Build, Train, Deploy
- Why Transformers Changed Everything: The Attention Revolution
- Data: The Foundation of Intelligence
- Deconstructing the GPT Blueprint: Every Component Explained
- Your Roadmap to Model Ownership: What You'll Build
Step 2: Text as Data
8 lessons
- Semantic Space: How Words Become Vectors
- Breaking Text into Intelligent Chunks: Tokenization Mastery
- Building the Model's Vocabulary: Token-to-ID Mapping
- Strategic Special Tokens for Context Control
- Byte Pair Encoding: The Production Standard (GPT-3/4, Claude, Llama)
- Efficient Data Sampling Strategies: Sliding Windows
- Learning Semantic Representations: Embedding Layers
- Position Encoding: Teaching Order to Parallel Systems
Step 3: The Attention Revolution
11 lessons
- Why Sequential Models Hit a Wall: The Case for Attention
- The Attention Mechanism: Weighted Relevance Explained
- Self-Attention: The Simplest Form (10 Lines of Python)
- Scaling Attention to Full Sequences: Batched Implementation
- Queries, Keys, Values: The Trainable Triplet
- Building Reusable Attention Components
- Causal Masking: The Secret of Text Generation
- Dropout: Preventing Attention Overfitting
- Building Production Causal Attention
- Why Multi-Head Attention Outperforms Single-Head
- Efficient Multi-Head Implementation: Parallel Computation
Step 4: Architecting Language Models
7 lessons
- Assembling the Complete Architecture: Embeddings → Transformer → Head
- Layer Normalization for Training Stability
- Feed-Forward Networks: The Other Half of Transformers
- Residual Connections: Enabling Deep Learning
- Building the Transformer Block: Modular Design
- Implementing the Full GPT Model: 4,000+ Lines You Understand
- Text Generation: Bringing Models to Life with Temperature Sampling
Step 5: Training at Scale
9 lessons
- Why Untrained Models Generate Noise: The Need for Pretraining
- The Loss Function: Measuring Learning (Cross-Entropy)
- Training vs Validation: Preventing Overfitting
- The Complete Training Loop: Forward, Loss, Backprop, Optimizer
- Temperature: Controlling Creativity (High = Creative, Low = Deterministic)
- Top-K Sampling: Quality Control for Generation
- Flexible Generation Functions: Customizable Decoding
- Persisting Model Weights: Deployment Readiness
- Leveraging Pretrained Weights: Loading GPT-2 for Transfer Learning
Step 6: Task Specialization
8 lessons
- The Fine-Tuning Landscape: Classification vs Instruction vs RLHF
- Data Preparation for Classification: Labeled Datasets
- Efficient Data Loading: PyTorch DataLoaders
- Transfer Learning Strategy: Freeze/Unfreeze Layers
- Adding Task-Specific Heads: Linear Projection Layers
- Training with Supervised Signals: Cross-Entropy on Class Distributions
- Fine-Tuning in Practice: 3-5 Epochs to Production
- Real-World Deployment: 95%+ Accuracy on Spam Detection
Step 7: Instruction Intelligence
9 lessons
- The Foundation of Helpful AI: How ChatGPT Was Created
- Formatting Instruction Data: (Instruction, Input, Output) Triples
- Batching Conversational Data: Padding and Attention Masks
- Building Instruction Data Loaders: Custom Collate Functions
- Choosing Your Starting Point: Pretrained vs From Scratch
- Training Instruction-Following Behavior: Supervised Fine-Tuning
- Capturing Model Responses: Generation and Evaluation
- Evaluating AI Assistant Quality: Helpfulness, Accuracy, Safety
- The Path to Alignment: RLHF and Beyond
Step 8: Production Training Excellence
6 lessons
- Warm Start: Preventing Early Instability with Learning Rate Warmup
- Cosine Annealing: Smooth Convergence with LR Scheduling
- Gradient Clipping: Explosive Gradient Protection
- The Production Training Function: Warmup + Cosine + Clipping + Logging
- GPU Optimization: Making Training 10x Faster
- Monitoring Training: TensorBoard and Weights & Biases
Step 9: Efficient Adaptation at Scale
7 lessons
- Low-Rank Adaptation Explained: How Modern ChatGPT/Gemini/Claude Fine-Tune
- Preparing Data for Efficient Training: Same Data, 10x Faster
- Injecting LoRA Adapters: Freezing Weights, Training Low-Rank Matrices
- Training with LoRA: 0.1% Parameters, 95-100% Performance
- Comparing LoRA vs Full Fine-Tuning: Cost-Benefit Analysis
- Multi-Task Adaptation: Swapping LoRA Adapters for Different Tasks
- Deployment Strategies: Serving Multiple Fine-Tuned Models Efficiently
Technologies You'll Master
Hands-on experience with industry-standard tools
Ready to Build Build Your Own LLM?
Join the next cohort and transform your understanding of AI. Stop being a consumer—become a builder.
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