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Consider autonomous model functionality from fundamental principles. Pre-trained LLMs generate sequential tokens containing compressed knowledge, yet lack practical instruction adherence, knowledge interrogation, or Python debugging capabilities. Additional refinement enables practical utility. Initial phase involves templating - demarcating input/output components so models comprehend task architecture. Examine chat templating illustration. Dialogue structures as alternating turns - our model must identify participants and content.
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