A large language model typically consists of:
: Sebastian Raschka (widely known for his machine learning educational content). Publisher : Manning Publications .
The training loop represents the most resource-intensive phase of the project. In 2021, training a model with billions of parameters was not feasible on a single machine; it required sophisticated distributed computing strategies. This involved Model Parallelism, where the model layers are split across different GPUs, and Data Parallelism, where the dataset is split and processed simultaneously. A critical algorithm introduced in this era was "ZeRO" (Zero Redundancy Optimizer) by Microsoft, which optimized memory usage by partitioning model states across data parallel processes. The training objective was typically autoregressive next-token prediction, where the model learns to predict the next word in a sequence, minimizing the cross-entropy loss over billions of tokens.
