Large Language Models have reshaped how we interact with machines—enabling tasks like code generation, creative writing, and question answering. However, most practitioners rely on pre‑trained models via APIs or libraries like Hugging Face. While convenient, this obscures the fundamental components: tokenization, autoregressive training, attention mechanisms, and optimization at scale.

: Convert raw text into smaller units (tokens) using algorithms like Byte Pair Encoding (BPE) or WordPiece.

Once the loss is low, how do you know if the model is "smart"? Your PDF should include:

To build an LLM, you must first master the , specifically the decoder-only variant used by models like GPT-4 and Llama 3. Key Components: