Fine-tuning in ML and AI

- Overview

Fine-tuning is a machine learning (ML) technique that involves adapting a pre-trained model to perform specific tasks. It's a subset of transfer learning, which is the process of using a model's existing knowledge to learn new tasks. 

Here are some reasons why fine-tuning is useful:

• Cost-effective: Fine-tuning is a cost-effective way to use existing models to create more specialized models.
  
• Efficient: Fine-tuning is a more efficient way to create models, especially when data is limited or computational resources are scarce.
  
• Improved performance: Fine-tuning can improve a model's performance on specific tasks, sometimes surpassing the original pre-trained model.
  
• Better suited to use cases: Fine-tuning can help adapt a model to suit more specialized use cases.

- The Steps for Fine-tuning a ML Model

Fine-tuning is the process of taking a pretrained machine learning model and further training it on a smaller, targeted data set. The aim of fine-tuning is to maintain the original capabilities of a pretrained model while adapting it to suit more specialized use cases.

Here are some steps for fine-tuning a model:

• Select a pre-trained model that's well-suited for the task
• Prepare sample data
• Fine-tune the model on the data
• Iterate on the model to improve its performance

One technique for fine-tuning models is low-rank adaptation (LoRA), which involves adding a low-rank matrix to the original matrix.