The Transformer Architecture (1 week)

The Transformer is the fundamental Neural Network architecture that enabled the evolution of Large Language Models as we know them now.

• The Self-Attention Mechanism
• The Multihead attention
• The encoder-decoder architecture
• The position embedding
• The layer-normalization
• The position-wise feed-forward network
• The cross-attention layer
• The language modeling head
• MoE (Mixture of Experts) https://monads.substack.com/p/mixture-of-experts-from-scratch

https://nn.labml.ai/?_gl=11s72hbt_gaMTM5ODQzMTU2MC4xNzYyOTYwNzU5_ga_PDCL9PHMHT*czE3NjI5NjA3NTgkbzEkZzEkdDE3NjI5NjA3OTUkajIzJGwwJGgw

Training LLMs to Follow Instruction (1 week)

GhatGPT, Claude, or Gemini are LLMs trained to follow human instructions. We are going to learn how those are trained from scratch:

• The Causal Language Modeling Pretraining Step
• The Supervised Learning Fine-Tuning Step
• The Reinforcement Learning Fine-Tuning Step
• Implementing those Steps with HuggingFace

How to Scale Model Training (1 week)

More than ever, we need efficient hardware to accelerate the training process. We are going to explore the strategy of distributing training computations across multiple GPUs for different parallelism strategies: