Building and Training a Small GLM-Style Model from Scratch

I explain how to build and train a 25-million-parameter GLM-style model on standard hardware, covering pretraining, vision components, and reinforcement learning. The examples also show how to frame research questions and evaluate controlled experiments; the focus is a small learning setup, not training a frontier-scale model.

- Model foundations: tokenization, transformer components, linear and sparse attention, and mixture-of-experts
- Training: language-model pretraining, a small vision example, and reinforcement-learning workflows
- Research practice: reward design, evaluation environments, experimental variables, and statistical considerations

Designed for learners and aspiring AI researchers, this walkthrough offers a practical starting point for studying model training and experimental design. Review the accompanying code, then try small experiments that vary one factor at a time and assess their results carefully.