Implement a Basic Tokenizer and Train a Simple LLM-like Model in a Notebook Case Study

Implement a Basic Tokenizer and Train a Simple LLM-like Model (notebook case study) You are given a partially completed notebook: a small Kaggle-style end-to-end workflow that loads raw text, tokenizes it, builds a vocabulary, encodes the corpus as ids, trains a tiny next-token model and samples from it. The main scaffold is already implemented. Fill in the missing components so the model trains and the printed loss generally decreases over the training steps. What is in the…