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These models pass input tokens through deep, multilayer transformer architectures that apply a sequence of mathematical operations to analyze context, weigh relationships, and determine likely outputs. Each layer refines the model’s understanding of the input, ultimately producing a prediction, 1 token at a time. This step-by-step token generation allows for highly accurate and contextually appropriate outputs, but also contributes to the computational intensity of inference workloads, especially for large models with many layers.
