LLMs
Master modern LLMs. Learn how GPT, Claude, and other large models work, train, and scale.
Prerequisites
Complete Level 5: Advanced Architectures
🎯What You'll Learn
- ✓How large language models are trained
- ✓Scaling laws and model size trade-offs
- ✓Fine-tuning and prompt engineering
- ✓Retrieval-augmented generation (RAG)
- ✓Multi-modal models combining text and images
💪Skills You'll Gain
🏆Learning Outcomes
📖Interactive Modules (10)
Language Model Scaling Laws
Understand scaling laws, how model size and data affect language model performance.
Tokenizer Comparison
Compare BPE, WordPiece, SentencePiece tokenizers and their impact on language models.
Positional Encoding Deep Dive
Learn positional encoding techniques that give Transformers understanding of sequence order.
Multi-Head Attention
Master multi-head attention, parallel attention mechanisms for richer representations.
Layer Normalization
Understand layer normalization and its importance in stabilizing Transformer training.
KV Cache Optimization
Learn key-value caching optimizations for faster LLM inference and generation.
Prompt Engineering Playground
Master prompt engineering, crafting effective inputs for large language models.
Fine-Tuning Strategies
Learn fine-tuning strategies: full fine-tuning, LoRA, and adapting LLMs to specific tasks.
RLHF Simulator
Understand Reinforcement Learning from Human Feedback (RLHF) for aligning language models.
Constitutional AI
Explore Constitutional AI, training models to be helpful, harmless, and honest.