These sources collectively explain that fine-tuning is a process of retraining a pre-trained Large Language Model on a specialized dataset to enhance its performance on particular tasks or domains. While it can significantly improve a model's responses, it's not ideal for injecting entirely new factual knowledge. Several methods exist for evaluating fine-tuned models, including general knowledge tests, human preference comparisons, and using another larger model to score responses. Due to the significant computational resources needed for full fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) techniques are increasingly popular, offering similar performance with considerably reduced computational costs and memory requirements by only training a small subset of parameters. Ollama, a local application, facilitates the use and evaluation of these models.