This article discusses best practices for fine-tuning large language models (LLMs) with a focus on model selection and curation. It highlights the importance of choosing the right base model, training dataset, and task complexity to achieve optimal performance. The article compares proprietary OpenAI models with open-source models, noting that open-source models have strong performance with less data but may require more data to reach higher performance ceilings. It also discusses factors such as ease of use, cost, control, and hyperparameter tuning when fine-tuning models. The article emphasizes the importance of using sensible defaults most of the time, keeping it simple, and having a robust evaluation suite to measure performance. Ultimately, it suggests that open-source models can provide significant cost savings and flexibility in deployment options, making them a viable alternative to proprietary models.