Building your own LLM: force it to guess billions of words until it ditches microwaves for fairy tale dragons

In the fifth session of the "Fazendo um LLM do Zero" series, the focus shifts to teaching a language model to generate human-like text through a process called pre-training. This phase is crucial in enabling the model to learn from vast amounts of data and adjust its neural connections accordingly. The pre-training process involves a repetitive loop where the model attempts to predict the next word in a sequence, receives feedback in the form of error correction, and adjusts its parameters to minimize the error rate. This is achieved through the use of cross-entropy, a mathematical concept that measures the difference between predicted and actual outcomes. The training data is divided into batches and epochs to manage the complexity of the task. After training, the model generates text based on probabilities, and strategies such as greedy search or temperature control can be employed to influence the output. Notably, checkpoints are used to save the model's progress, ensuring that training can be resumed in case of interruptions.