We compare full-parameter fine-tuning with LoRA (Low-Rank Adaptation of Large Language Models) and explore their strengths and weaknesses. We train Llama 2 models on the same three real-world use cases as in our previous blog post to provide a baseline for task-specific performance, hardware requirements, and cost of training. The results show that using LoRA involves a trade-off between serving efficiency and model quality, which varies according to the specific task at hand. Additionally, we offer insights into how to stabilize training with LoRA through intelligent prompting techniques. We further demonstrate that adopting a lower learning rate can enhance the reliability of the resulting model checkpoints. Our experiments show that LoRA fine-tuned models are only slightly worse than full-parameter fine-tuned models for tasks like generating SQL queries or text-based functional representations, but fall short in mathematical reasoning tasks. By leveraging LoRA's efficiency in memory and serving, we can deploy multiple fine-tuned models simultaneously while reducing storage requirements, making it a promising alternative to full-parameter fine-tuning, especially when operating on cheaper lower-memory instances or with larger context lengths.