Small Language Models
Explore the advantages of experimenting with smaller language models, which can be efficiently trained on a single GPU. With sizes ranging from 111 million to 13 billion parameters, these models allow for domain-specific natural language generation tasks while adhering to Chinchilla scaling laws. The discussion highlights the practical limitations of training extremely large models, emphasizing the importance of compute efficiency and data availability.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
676: The Chinchilla Scaling Laws — with Jon Krohn (@JonKrohnLearns)
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