The journey to create a state-of-the-art large language model (LLM) begins with a process called pretraining. Pretraining a state-of-the-art model is computationally demanding, with popular open-weights models featuring tens to hundreds of billions parameters and trained using trillions of tokens. As model intelligence grows with increasing model parameter count and training dataset size��
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