How to fine-tune a model using Axolotl

Model fine-tuning involves adapting pre-trained machine learning models to enhance their performance on specific tasks, leveraging existing knowledge for efficiency and improved outcomes, and is particularly useful when data is limited. This process is exemplified in a blog post detailing how to fine-tune models using Runpod and Axolotl, an open-source tool, with a focus on deploying a pod and employing LoRA (Low-Rank Adaptation) to efficiently adapt models like Llama 3. The guide covers steps from deploying the pod, exploring the workspace, to fine-tuning using specific configuration files, and testing model outputs against expected results, highlighting LoRA's efficiency despite lower precision. It suggests further experimentation with full fine-tuning and QLoRA for potentially more accurate results, while also advising on cost management by terminating unused pods.