Understanding Language Model Sizes and Functionality

This chapter explores the varying sizes of large language models, distinguishing between small and large based on parameter counts, and introduces Hugging Face as a useful platform for implementing these models. It delves into the processes of training and inference, the concept of tokenization, and the implications of context limitations in AI conversations. Additionally, the chapter highlights the evolution of language models, drawing comparisons with traditional search engines and explaining the significance of the transformer architecture and real-time data integration.

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