Language Models 101: Pretraining and Fine-Tuning

This lesson walks through the fundamentals of language models as the opening module in H2O's LLM learning path, aimed at both technical and non technical learners. It explains how a language model predicts sequences of words by learning statistical patterns from large amounts of text, and distinguishes pretraining on unlabeled text from fine tuning on a smaller labeled dataset for a specific task. The lesson also surveys the techniques behind language models over time, from simple n-grams and hidden Markov models to recurrent neural networks and transformer based architectures like GPT.