A beginner-friendly walkthrough of how an LLM actually works end-to-end: from typing a prompt to receiving a response â covering tokenization, embeddings, Transformer layers, KV cache, the training loop, embeddings for search, and why decoder-only models won.
Two strategies to shrink LLMs â one compresses weights, the other transfers knowledge. A practical guide to distillation and quantization: when to use each, how to implement them with Hugging Face, and why the real answer is both.
A deep dive into the Transformer architecture â how attention connects tokens and why the Feed-Forward Network is the real brain of the model. Plus the key to understanding Mixture of Experts (MoE).