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created_time: 2025-11-28T16:58:18.393Z
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ocr_text: Additionally, I'm thinking about the computational resources required for fine-tuning. Since LLMs are large models with many parameters, training them on specific ta\nsks might require more powerful hardware and longer computation times. But given that they're already trained on vast amounts of data, it's likely that fine-tuning i\nsn't too computationally intensive once optimized\nIn summary, fine-tuning in the context of LLMs involves adapting a pre-trained model to understand specific tasks by providing relevant prompts or guidance during tr\naining. This allows the model to not only generate coherent text but also answer questions and perform other tasks aligned with user intent. It's an important step i\nn leveraging existing knowledge for better performance, balancing efficiency with effectiveness.\n</think>
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