The article demonstrates the fine-tuning of the LLaMA 2 - 70B model using Monster API's no-code LLM finetuner, which reduces costs and manual effort. The fine-tuned model is used for instruction-finetuning on the Databricks Dolly V2 dataset, achieving impressive results with a training loss and evaluation loss curve showcasing substantial progress and improvement in the model's performance. The cost analysis highlights significant cost savings compared to traditional cloud platforms, with Monster API's no-code approach streamlining the fine-tuning pipeline and reducing both time and manual effort. The article concludes that this no-code approach makes it easier for developers to harness the power of large language models, driving advancements in natural language understanding and AI applications.