‘Inheritune’ by UT Austin Assists Efficient Language Model Training: Leveraging Inheritance and Reduced Data for Comparable Performance

Scaling up LLMs presents significant challenges due to the immense computational resources needed and the need for high-quality datasets. Typically, the pre-training process involves utilizing models with billions of parameters and training them on datasets containing trillions of tokens. This intricate procedure demands substantial computational power and access to high-quality data to achieve better performance […]

The post ‘Inheritune’ by UT Austin Assists Efficient Language Model Training: Leveraging Inheritance and Reduced Data for Comparable Performance appeared first on MarkTechPost.

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