Fine Tuning Large Language Models

Artificial Intelligence is an iterative process- to work well it needs to be refined and checked as it develops. In a previous article, I explained how fine-tuning your data is crucial for leveraging LLM and SLM in your business.

Fine-tuning is a process of training a large language model that has been pre-trained on a general dataset with a smaller, task-specific dataset. This new dataset has labeled examples that are relevant to the target task. To fine-tune a large language model, you need to follow these basic steps:

• Decide the Task: Define the specific task that you want the model to perform. It could be anything from sentiment analysis to text generation.
• Gather Data: Collect a dataset that is relevant to your task. This dataset should have labeled examples that the model can learn from.
• Model Selection: Choose a pre-trained language model that is suitable for your task. Some popular pre-trained language models are BERT, GPT-3, and RoBERTa.
• Fine-Tuning: Train the pre-trained model on your task-specific dataset. This involves updating the weights of the pre-trained model using your dataset2.
• Evaluation: After fine-tuning, evaluate the performance of the model on a separate test dataset.

Fine-tuning works best when you have a small dataset, and the pre-trained model is already trained on a similar task or domain. You can also try advanced fine-tuning techniques like multitasking, instruction fine-tuning, and parameter-efficient fine-tuning.

It is also important to highlight another technique.

Transfer learning is a technique that uses a model that has already been trained on a large dataset as a basis for a new task or domain. The goal is to use the knowledge that the pre-trained model has learned from the large dataset and apply it to a related task that has a smaller dataset. Transfer learning usually consists of two main steps.

1. Feature Extraction: We use the pre-trained model as a fixed feature extractor. We remove the final layers responsible for classification and replace them with new layers that are specific to our task. The pre-trained model’s weights are frozen, and only the weights of the newly added layers are trained on the smaller dataset1.
2. Fine-Tuning: Fine-tuning takes the process a step further by unfreezing some of the pre-trained model’s layers and allowing them to be updated with the new dataset. This step enables the model to adapt and learn more specific features related to the new task or domain1.

In summary, while transfer learning freezes all the pre-trained layers and only trains the new layers, fine-tuning goes a step further by allowing the pre-trained layers to be updated. Both techniques are powerful and allow us to leverage pre-trained models in machine learning and deep learning tasks.