Introduction
A foundation model is trained on broad data so it can support many later uses, directly or with adaptation. The name tells us about breadth and reuse. Evaluate capability, safety and generality separately for each system. Large language models are one family; other models work with images, audio, video, or combinations of them.
We will follow the language-model process step by step. Text becomes tokens, tokens become numerical representations, layers transform those representations, and the model scores possible next tokens. A decoding procedure selects output while the application supplies context, tools, and controls. Following the chain makes model claims easier to question.
Revisit vectors, matrix multiplication, parameter updates, and training versus inference in Module 1, plus memory and throughput in Module 2. The module explains the main transformer components and how they fit together. The small calculations explain what each operation contributes. Work through representation and attention, training and generation, then context and deployment. Your project combines a model explanation with a controlled comparison experiment.