Deploying large language models (LLMs) effectively to address real-world challenges requires careful consideration of scaling strategies. While increasing model size and training data can often lead to performance improvements, it's crucial to also fine-tune model architectures for specific tasks and domains. Furthermore, harnessing the power of di

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