Cerebras-GPT: A Family of Efficient Language Models (arxiv.org)

Summary:

• Researchers improve large language models through efficient pre-training and scaling.
• Cerebras-GPT models show state-of-the-art training efficiency on both pre-training and downstream objectives.

Key terms:

• Pre-training: initial training phase of a language model using a large dataset
• Scaling: increasing the size and complexity of a model to improve its performance
• Cerebras-GPT: a family of efficient language models with improved performance
• HuggingFace: a platform for sharing and using pre-trained language models

Tags:

Research Tools HuggingFace Cerebras-GPT Open Compute Optimal Power Law Scaling Hyperparameter Predictability Compute Optimal Reproducibility Large Models