large_language_model_training_playbook
This playbook serves as an extensive resource for large language model training, covering topics such as model architecture, parallelism, and precision considerations. It provides practical guidance on hyper-parameter selection, managing training instabilities, and optimizing throughput. Complementing the LLM Training Handbook, it delves into intricacies of learning rate schedules, batch size adjustments, and key performance metrics, offering insights for a comprehensive understanding of successful model training dynamics, including how to address software and hardware debugging challenges effectively.
