Explore the inner workings of Large Language Models, from pretraining and tokenization to neural networks, inference, and reinforcement learning.

• introduction
• pretraining data (internet)
• tokenization
• neural network I/O
• neural network internals
• inference
• GPT-2: training and inference
• Llama 3.1 base model inference
• pretraining to post-training
• post-training data (conversations)
• hallucinations, tool use, knowledge/working memory
• knowledge of self
• models need tokens to think
• tokenization revisited: models struggle with spelling
• jagged intelligence
• supervised finetuning to reinforcement learning
• reinforcement learning
• DeepSeek-R1
• AlphaGo
• reinforcement learning from human feedback (RLHF)
• preview of things to come
• keeping track of LLMs
• where to find LLMs
• grand summary