Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) are key methods used to align Large Language Models (LLMs) with specific tasks and human preferences, with SFT initially teaching models desired skills and RLHF refining model responses based on human-like scoring. High-quality datasets are essential for both methods, with SFT requiring prompt-response pairs and RLHF necessitating ranked responses for the same prompt. The Labelbox platform aids in creating these datasets efficiently, while Parameter-Efficient Fine-Tuning (PEFT) techniques help manage computational demands by limiting the number of trainable parameters, making fine-tuning feasible even on single-GPU machines. PEFT employs various strategies like additive, selective, and reparametrization-based methods, such as LoRa, to optimize memory and computational efficiency. Hugging Face's PEFT library provides tools to implement these techniques, enhancing the practicality of fine-tuning large LLMs like Meta's Llama models.