Fine-tuning LLMs typically involves using supervised training methods on data that is extensive and domain-specific.
Labelled data consist of input-output pairs where the inputs are the data points and the outputs are the labels. The labels provide accurate versions of the answers that the model aims to predict.
The data should be clean and representative of the tasks the model is going to perform.
It should be structured in a consistent format, typically as a JSONL document, where each line represents a prompt-completion pair corresponding to a training example.
Techniques like cross-validation can help prevent overfitting by splitting the training data into smaller parts to validate the model's performance and regularly testing it on a separate validation set.
Once your model has been fine-tuned, it is essential to test and evaluate its performance using a separate dataset. This step helps ensure that the model is performing as expected and can effectively address your organization's specific needs.