The text discusses the importance of pre-training in large-scale language models, which is a crucial step in developing their language understanding and generation capabilities. Pre-training involves training the model on extensive datasets using self-supervised learning techniques such as masked language modeling or autoregressive language modeling. The goal of pre-training is to imbue the model with a broad understanding of language structure and knowledge, enabling it to generalize effectively when fine-tuned for specific applications. However, pre-training also presents several technical challenges, including computational resource demands, data acquisition and processing, and training duration. To address these challenges, instruction-pre-training has been proposed as a novel method that augments the unsupervised training corpus with instructions to enhance the model's performance. Instruction-pre-trained models have been shown to outperform vanilla pre-trained models on various tasks, making them a promising approach for developers who struggle with hardware limitations and budget constraints. The text also provides an example of how to perform instruction-pre-training using the Monster API, which allows users to seamlessly convert their unlabeled corpus into instruction-augmented pre-training corpora suitable for pre-training.