Training a large language model involves a multi-step process. It begins with collecting a diverse and high-quality dataset. The dataset is then preprocessed and tokenized to prepare it for training. Next, the model architecture and hyperparameters are defined. The model is trained on powerful hardware with specialized deep-learning frameworks. The training process involves iterative optimization of the model's parameters using techniques like backpropagation and gradient descent, eventually resulting in a trained and fine-tuned language model. For further information, visit AIMultiple.