Original Video Link: State of GPT | BRK216HFS
Summary
GPT Assistant training pipeline
AI researcher and founding member of OpenAI, Andrej Karpathy, discussed the training of large language models like GPT in a two-part presentation. In the first part, he described the training process of GPT Assistants, highlighting a four-stage pipeline: pretraining, supervised finetuning, reward modeling, and reinforcement learning.
During pretraining, most of the computational work happens. It involves training on Internet-scale datasets with thousands of GPUs over a period of months. The process begins by collecting a large amount of data from diverse sources such as web scrapes, GitHub, Wikipedia, and more. This data is tokenized and turned into sequences of integers, the native representation for GPTs.
He elaborated on the pretraining phase by providing examples of hyperparameters used in this stage using GPT-3 and Meta's LLaMA models. These models deal with a vocabulary size of tens of thousands of tokens and context lengths that can go up to 100,000, with the number of parameters ranging in the billions.
The data batches formed during pretraining are fed into a transformer neural network, which aims to predict the next token in a sequence. Over time, with iterative training, the transformer can learn and make more coherent and consistent predictions, generating increasingly sophisticated language.
After pretraining, the model undergoes a fine-tuning process. Karpathy notes that these base models learn powerful general representations that can be efficiently fine-tuned for a variety of downstream tasks. These tasks can include anything from sentiment classification to question-answering systems, leveraging the versatile multitasking ability of the transformer model.
The talk marked an evolution in the understanding of model prompting, demonstrating how the models could be guided to perform specific tasks effectively without additional fine-tuning. The presentation served as an in-depth exploration of the processes behind the training of large language models, setting the stage for further developments in AI.