🧠 Interactive Transformer Architecture Tutorials

Learn transformer architecture concepts through hands-on visualizations and step-by-step mathematical analysis

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🏛️ Foundation Tutorials
Essential concepts and architectural understanding
🏗️ Transformer Basics: The Foundation Start Here
Essential foundation for understanding modern AI - from the revolutionary breakthrough to why transformers work so well. Covers the core architecture, three paradigms (BERT/GPT/T5), and interactive comparisons with older architectures.
Attention mechanism • Parallel processing • Architectural paradigms • AI evolution
📊 Architecture Comparison: Modern LLM Designs New
Comprehensive comparison of modern LLM architectures across the industry. Real model analysis of GPT-4, Claude, Gemini, LLaMA, and more with design decisions breakdown and performance trade-offs.
Model comparison • Design trade-offs • Production considerations • Architectural evolution
🎯 Q, K, V Matrix Dimensions
Interactive exploration of attention mechanism matrix sizes and their relationship to model architecture with real model comparisons, matrix size calculations, and architecture analysis.
Attention matrices • Model dimensions • Memory scaling • Architecture comparison
🌀 RoPE: Rotary Position Embedding
Comprehensive guide to understanding how transformers encode position information through rotation with visual dimension pairing, complete mathematical walkthrough, and interactive examples.
Position encoding • Dimension pairs • Rotation mathematics • Context scaling
🔧 Fine-tuning Mastery Series
Complete guide to efficient model adaptation and customization
🔍 LoRA: Low-Rank Adaptation Mathematics Series 1
Complete mathematical foundation of LoRA - the breakthrough technique for efficient fine-tuning. Interactive parameter calculator, matrix decomposition visualizer, and production deployment strategies.
Low-rank decomposition • Parameter efficiency • Rank selection • Adapter strategies
🎛️ Full Fine-tuning vs LoRA: Complete Comparison Series 2
Master the complete spectrum of fine-tuning approaches. Interactive layer freezing, catastrophic forgetting analysis, memory calculators, and smart decision framework for optimal approach selection.
Full fine-tuning • Layer freezing • Catastrophic forgetting • Resource optimization
🚀 Advanced PEFT: QLoRA, DoRA & Modern Techniques Series 3
Cutting-edge Parameter-Efficient Fine-Tuning techniques. QLoRA's 4-bit quantization, DoRA weight decomposition, AdaLoRA adaptive allocation, and latest research developments.
Quantization mathematics • Advanced PEFT • Deployment optimization • Latest research
⚡ Core Mechanisms
Deep dives into transformer internals and processing
⚡ Complete Attention Mechanism
Interactive step-by-step walkthrough of how Q, K, V matrices work together in transformer attention, from matrix creation through final output with real examples.
Q×K^T computation • Softmax normalization • Attention×V • Matrix interactions
🔄 Attention Mechanisms Evolution: MHA → GQA → MLA
Complete evolution of attention mechanisms with KV caching foundation, memory optimization techniques, and deep dive into compression mathematics across all variants.
KV caching • Memory optimization • Grouped attention • Compression techniques • Evolution timeline
🚀 Text Generation Process
Complete mathematical walkthrough from attention output to next token prediction, including feed-forward networks, layer normalization, vocabulary projection, and sampling strategies.
FFN computation • Matrix flows • Vocabulary logits • Sampling strategies • Performance analysis
🚀 Advanced Topics
Scaling, optimization, and cutting-edge techniques
🎯 Mixture of Experts: Scaling Transformers Efficiently
Interactive exploration of MoE scaling through sparsity, routing mechanics, expert specialization, load balancing, and real-world model analysis with cost-benefit considerations.
Sparse computation • Expert routing • Load balancing • Parameter scaling • Real MoE models
📊 Context Length Impact: Training vs Inference
Mathematical analysis of why models trained on long contexts excel at shorter sequences with fixed vs dynamic components, RoPE frequency analysis, and performance metrics.
Context extension • Performance analysis • RoPE frequencies • Training vs inference

✨ Tutorial Features

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Responsive Design
Works on desktop, tablet, and mobile
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Interactive Visualizations
Real-time calculations and demonstrations
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Mathematical Precision
Step-by-step formulas with actual numbers
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Real Model Data
Architecture specs from production models
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Configurable Examples
Adjust parameters to see immediate effects
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Production Ready
Deployment strategies and resource planning

🎯 Target Audience

🎓 Recommended Learning Path

🏛️ Foundation Phase

  1. 🏗️ Transformer Basics - Understand the revolutionary breakthrough and foundation
  2. 📊 Architecture Comparison - Learn how modern LLMs differ and why
  3. 🎯 Q, K, V Matrix Dimensions - Understand the basic building blocks
  4. 🌀 RoPE: Rotary Position Embedding - Learn how position is encoded

⚡ Core Mechanisms Phase

  1. ⚡ Complete Attention Mechanism - See how Q, K, V work together
  2. 🔄 Attention Mechanisms Evolution - Learn memory optimization and scaling techniques
  3. 🚀 Text Generation Process - Complete pipeline from attention to tokens

🔧 Fine-tuning Mastery Phase

  1. 🔍 LoRA Mathematics - Master the most popular PEFT technique
  2. 🎛️ Full Fine-tuning vs LoRA - Complete comparison and decision framework
  3. 🚀 Advanced PEFT - Cutting-edge techniques (QLoRA, DoRA, etc.)

🚀 Advanced Topics Phase

  1. 🎯 Mixture of Experts - Advanced scaling through sparse computation
  2. 📊 Context Length Impact - Advanced concepts about training vs inference

🛠️ Technology Stack

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