The tutorial highlights the process of fine-tuning the new open-source large language model (LLM) Mistral 7B for summarization tasks using the Ludwig framework. Despite the base model's initial poor performance in domain-specific tasks, fine-tuning it using Ludwig's "low-code" interface enhances its summarization capabilities significantly. The article emphasizes recent advancements in techniques like LoRA and QLoRA, which enable efficient fine-tuning by reducing memory requirements with minimal accuracy loss. These innovations, alongside open-source models like Llama 2, democratize access to LLMs, allowing businesses of various sizes to integrate AI effectively and cost-efficiently. Additionally, the tutorial provides a step-by-step guide for training and validating models in Google Colab environments, showcasing the potential for high-quality output even with limited computational resources.