How to Train Large Models on Many GPUs?

Feb 11, 2023 ·

The article by Lilian Weng explores the challenges and strategies for training large language models. Weng examines how increasing model size can have a positive effect on accuracy, but also leads to a rise in training complexity and difficulty. She explains that one of the primary factors affecting model size is the availability of data. Weng notes that larger datasets are necessary to effectively train large models, and suggests ways to increase the dataset size through data augmentation and using extra datasets. Additionally, she discusses strategies such as pretraining and transfer learning to address the issues of having insufficient data. These strategies help reduce the amount of resources needed for training large models. Weng also covers topics like hyperparameter optimization, memory considerations, distributed training, and activation function selection. This article provides insight into the challenges of training large language models and offers numerous strategies to make the process more effective. Overall, it is an informative article that provides helpful guidance on training large language models.