What Is Fine-Tuning?

What Is Fine-Tuning?

Fine-tuning is a special machine learning technique to fine-tune a pre-trained model. Such pre-trained models have already learned general patterns from large datasets. However, in fine-tuning, the model is then further trained with a smaller dataset which is highly relevant to the task at hand.

Fine-tuning can be particularly useful for large and complex models, since training a model of this type from scratch would require a large amount of data as well as substantial computing power and time to train. Fine-tuning instead allows for rapid specialization of an already powerful general model.

This additional training can be used to teach a model to deal with certain topics, to perform certain tasks, to write in a certain style or to produce output of a certain type. So a general purpose language model could be fine-tuned to be used for things like coding, customer service, legal documents and more. This is usually referred to as transfer learning, because you are transferring knowledge from a previously trained model to another.

How Does Fine-Tuning Work?

In the fine-tuning process, the model has already gone through the pre-training process. Therefore, the model is not initialized randomly from scratch. Instead, it uses the model weights after pre-training as the initial model.

After setting up the pre-trained model for fine-tuning, the next step is the preparation of a relatively small dataset that is relevant to the intended task and includes examples of the type of information, language, etc. that the model should learn.

During fine-tuning, your model will go through the examples of your fine-tuning data set. It will adjust the parameters of the model in order to improve its performance on the specific task you have defined. This can be done in a supervised, self-supervised, semi-supervised or even reinforcement learning manner.

During fine-tuning, developers typically repeat the training process for several iterations. They test the results of the previous iteration and can also change learning rate, the size of the dataset and other parameters to improve the results of the current iteration.

Even a small dataset of relevant examples can be better than a large one with low quality data. You can also try to prepare the data as best as possible for the fine-tuning.

Fine-Tuning vs. Pre-Training

The distinction between pre-training and fine-tuning is the major difference between the two processes.

The primary goal of the pre-training phase is to build a model that acquires general knowledge and basic functionality of a wide scope. Usually, an enormous amount of data is used for the model’s training, which often requires large amounts of computing power and significant funds.

The next stage of the model’s development is fine-tuning, which uses the pre-trained model as a starting point. The fine-tuning process uses a much smaller dataset, often containing only a few hundred to thousands of instances, which are specifically designed for the particular task or domain the model is to be applied to.

To summarize: pre-training a model builds broad capabilities. Fine-tuning then specializes this existing capability for a specific use case.

What Are the Main Types of Fine-Tuning?

Depending on how much of the pre-trained model has to be changed and how specialized the target application is, fine-tuning can be performed in different ways.

Full Fine-Tuning: Full fine-tuning means that the entire model is fine-tuned. The model is then fine-tuned from scratch on a specialized dataset for the desired application, allowing the model’s weights to adapt very broadly to the task at hand.

The risk of the model losing generalization capacity which it acquired during pre-training has to be avoided by choosing the appropriate training settings.

Parameter-Efficient Fine-Tuning

Parameter-efficient fine-tuning (PEFT) is another approach for fine-tuning. Here, instead of fine-tuning the whole model, only a small part of the model is updated. This approach has lower computational and memory requirements than full fine-tuning.

By only updating a fraction of the model’s parameters, parameter-efficient fine-tuning (PEFT) can reduce computational costs and memory requirements significantly. A large language model, for example, can be fine-tuned for many different applications without having to re-train the entire model each time.

This can be done using methods such as LoRA, or Low-Rank Adaptation, for example.

The decision between Full Fine-Tuning and Parameter Efficient Fine-Tuning depends on the specific model, available computational resources, dataset and end goal.

What Data Is Used for Fine-Tuning?

The data for fine-tuning depends on the specific goal of the fine-tuned model.

For a language model, this could be questions and answers, coding examples, domain-specific documents, instructions, or even writing examples that the model should follow.

This is how additional data is used for fine-tuning image classification models, for example with labeled images of different categories that the model needs to recognize better after fine-tuning.

This data set should resemble the application as much as possible, in order to test the fine-tuned model on examples that are as similar as possible to the examples it will encounter in real life use.

Incorrect labels, duplicated examples, inconsistent formatting and even irrelevant information are all problems that can affect the model’s performance after fine-tuning.

Where Is Fine-Tuning Used?

The use of fine-tuning is widespread in many areas of AI. Firstly, general-purpose models such as LLMs (Large Language Models) can be fine-tuned for specific applications such as coding.

Large Language Models

The method of fine-tuning is frequently applied to very large language models (LVMs), since these general purpose language models can be fine-tuned to perform a large range of tasks, including coding, classification, and more, in a variety of formats such as structured output, and domain specific language.

An example for fine-tuning a Large Language Model would be training it on a set of programming requests and corresponding code examples in order to improve performance on coding related tasks.

Customer Support

Organizations can fine-tune a model for a specific support task, for a certain communication style or for customer input of a certain kind.

Specialized examples can be used for training models for customer support. However, fine-tuning does not automatically make a model aware of constantly changing business information. This information is typically retrieved from connected knowledge sources.

Healthcare

For healthcare, this could mean fine-tuning a general language model to read specialized medical language or complete certain healthcare-related tasks and document types.

Care must be taken in validating any fine-tuned model as well as ensuring that appropriate privacy measures are in place, and human oversight is in place to handle exceptions that may arise due to errors.

Finance

Specialized models are developed for financial organizations to perform tasks such as financial terminology, document classification and financial analysis within specific applications.

The training data and the evaluation of a financially fine-tuned model must also reflect the specific use case of the financial application.

Software Development

Fine-tuning can also be applied to software development, in order to better adapt a language model to programming languages, coding conventions, documentation generation and more.

This can be useful to increase the performance of a general model in a very specific technical environment.

Content and Communication

The same is true for content generation: a model can be fine-tuned in order to produce content that is of a certain length and in a certain format.

For example, one can add training examples for a specific structure or even a specific tone. The goal here is not to create output that is significantly different, but rather to make the model fit the specific requirements of the application.

What Are the Benefits of Fine-Tuning?

Fine-tuning allows a company to build upon a model that already exists, in order to reduce the amount of data, and computational resources required to build a specialized model for use within a given application.

Specialized training can also be used to improve performance on specific tasks, a general-purpose AI model often does not perform equally well on specialized domains. Since a focused training set has a large number of examples of the relevant input and output the model can learn to handle these cases very well.

Another benefit is that Fine-Tuning can also be used for Customization of a model, to adapt to special tasks, to follow special patterns or simply to be used in a special domain.

There is also a practical advantage to building on top of a strong foundation model. Rather than having to train a large model from scratch, an organization can leverage the foundation model and fine-tune it for their specific use case.

What Are the Challenges of Fine-Tuning?

Fine-tuning a model is not a matter of simply throwing more data at the model and running the new data through training again. The fine-tuning data set must be chosen carefully.

However, a model can overfit to the fine-tuning data in such a way that performance drops severely on new input instances (overfitting).

There is also the risk of the model forgetting parts of the knowledge it had acquired prior to fine-tuning. The larger the fine-tuning dataset in comparison to the pre-trained model, the greater this risk.

As with training from scratch, the fine-tuning process is sensitive to many aspects of the training configuration, such as the learning rate, batch size, regularization, and the size of the fine-tuning dataset.

When fine-tuning a model it’s typically important to test it on a good amount of data that mimics how you intend to use the model. This will give you a better idea of how well it’s performed than just training and testing on the data that you fine-tuned with.

When Should You Use Fine-Tuning?

This method of fine-tuning is most useful when a model has already been pre-trained with general-purpose capabilities that it can apply to many different tasks. However, the performance of the model can then be fine-tuned for specific tasks, domains, or behaviors.

For specific tasks and certain domains, it is required to have a model which consistently generates output of the same kind and with strong adherence to given terminology and style.

Fine-tuning is not always the best option for all tasks. Connecting a model to a suitable knowledge source is often sufficient to tackle problems for which the model doesn’t have access to the required current or private information.

Equally important is the fact that by using carefully worded prompts a program can be made to perform in ways which would otherwise require it to be fine-tuned.

The most important thing to consider is what you want to change in your model. Do you want the model to learn a new recurring behavior or do you want it to learn a new specialization? Or maybe you just need to feed the model some new information?

Fine-Tuning in Large Language Models

As we explored in previous sections, when working with Large Language Models (LLM), such models typically go through a pre-training phase on a very large, general-purpose dataset. Fine-tuning then follows by training the LLM on a smaller dataset that is very relevant to the specific use case at hand.

The pre-training of an LLM involves learning to understand language from large datasets. The fine-tuning of an LLM then involves exposing it to examples of the specific task that it will be used for.

A general language model could for example be fine-tuned for programming in a large-scale dataset of coding examples. Or a model could be fine-tuned for specific response formats or even communication styles.

The training process does not necessarily replace the model’s general capabilities, it rather specializes it for a specific use case. In other words, the training focuses the model’s general capabilities on the intended application.