Choosing the right Large Language Model (LLM) for text summarization and code generation is crucial due to their varying strengths and weaknesses. The process involves narrowing down model choices, evaluating pre-trained models, and fine-tuning them on specific datasets to optimize performance. For text summarization, LLMs like LLaMa, Gemma, and Falcon are strong contenders, with LLaMa being a versatile choice that can achieve multiple tasks, while Gemma excels in efficiency and versatility. For code generation, models such as Mistral 7B, Mixtral, CodeLlama, and the latest LLaMa models are of interest, with CodeLlama being exceptional in understanding complex programming tasks and generating syntactically correct code. Fine-tuning these models requires careful consideration of hyperparameters and rigorous testing to achieve optimal results. By following a case-study approach, developers can optimize their workflow to fine-tune and deploy an LLM that excels in specific applications.