AI Distillation: Making Big Brains Smaller

Large language models (LLMs) are powerful tools, but they need a lot of resources. Knowledge distillation compresses these large models into smaller ones that work on devices with limited power. It's like learning from a wise teacher and then summarizing that knowledge into a smaller, easy-to-use notebook.

What is Knowledge Distillation?

Knowledge distillation is a training method where a small model, called the student, learns from a larger, more powerful model, called the teacher. The teacher model has already learned a complex task, and its internal knowledge is used to guide the training of the student model. This is different from training a model from scratch where it learns just from data. With distillation, the student model learns from the teacher’s way of handling data, not just the data itself. This enables the student to perform closer to the teacher but with smaller size and less processing need.

Why Distill Models?

There are several key reasons for using distillation. First, it allows the deployment of complex models on devices with limited resources. Running a large model on a phone, for example, would be slow and drain the battery rapidly. A distilled model can offer similar performance without such cost. Second, it speeds up the process. Because they are smaller, distilled models are faster to run, which is beneficial for real-time applications. Finally, it can improve the generalization of smaller models. Learning from a teacher that has a high level of competence can actually teach a small model better than just training it directly.

How to Distill an LLM: A Step-by-Step Guide

Distilling an LLM is a process that includes several stages:

1. Choosing the Teacher and Student Models

You need to select both the large, complex teacher model, and the smaller student model you plan to train. The student should be simpler than the teacher but must have the capacity to learn the necessary information. For example, a BERT-large model might be the teacher, and a smaller BERT-base model can be the student.

2. Preparing the Data

You'll need data relevant to the task you want both models to perform. This can be the same training data used for the teacher, or a different set that is more appropriate for the student. The data should be structured in a way that can be fed into both the teacher and the student models.

3. Generating Soft Labels

Here is where the teacher model comes in. Run the teacher model on the data and get the predicted output probabilities. These probabilities are known as "soft labels" because they give more information than "hard labels" (simply the final classes). For example, instead of saying, "the answer is cat", the teacher might say something like, "there is a 90% probability of a cat, an 8% chance of a dog, and a 2% chance of a bird". These probabilities show the teacher’s confidence in each output option.

4. Training the Student Model

Now comes the student training phase. The student model is trained using two things together: the original hard labels from the data and the soft labels generated by the teacher model. The training involves an objective function that tries to match both labels at the same time. For example, you might use a cross-entropy loss for matching the hard labels and also a Kullback-Leibler divergence to match the soft labels. This way the student learns to make predictions that are like both the teacher's output and the original data.

5. Evaluating the Student Model

Once training is done, evaluate the performance of the student model using the test dataset to see how well it has learned to perform the task. Performance can be measured using standard tests for the task the model was trained to complete. If the performance is not enough, you might need to adjust some hyper parameters such as learning rate or the structure of the student model itself.

Technical Points and Examples

The key to distillation lies in the "soft labels." These aren't just the final answers; they show how the teacher model thought through the problem. The teacher may have had many possible solutions, and the soft labels express these as probabilities. Think of it like this: instead of just giving you the answer to a math problem, the teacher also shows you the steps they took to get there, including some approaches that were close but not quite right. These steps, even the close ones, give extra information that helps you learn better.

For instance, imagine a model trained to recognize pictures of animals. If the teacher model sees a picture of a cat, it might say, "I'm 90% sure it's a cat, 8% sure it's a small dog, and 2% sure it's something else". This "8% for a dog" is important. It shows the teacher knows a cat and a small dog can look similar, and this helps the student to learn the finer differences too.

The training of the student model is a combination of two things. First, the student tries to give the right answer, just like any other model training. But second, it also tries to copy the teacher’s thought process, using the soft labels. So, it's not only trying to say "cat", it's also trying to say "90% cat, 8% dog, 2% other," if that was what the teacher said. To make this happen, a "temperature" setting is often used. It’s like adjusting the focus of a camera. It makes the teacher's "thought process" clearer. Think of it as turning up the contrast a bit, so that the student sees more clearly those slightly different probabilities, and this gives better learning results.

Let's look at another example. We have a large model that understands the mood of a sentence. It looks at "The food was delicious, but the service was slow" and gives 80% "positive" and 20% "negative" sentiment. The student also looks at this sentence and its simple label is “positive”. Now with soft labels, the student will try to not only predict positive sentiment, but also a small probability for negative, to copy the teacher’s output. So the student can learn the complexity of the sentence, where both positive and negative meanings exist.

Final Words

Knowledge distillation is a valuable approach to training smaller AI models that retain most of the performance of large models. This allows for the use of advanced AI tools in many applications where there is a limitation on computer resources. The key points are the selection of the teacher and the student, generating helpful soft labels from the teacher model and training the student so that it has an understanding of how the teacher model works with the data. By using the steps described above, one can successfully distill a large language model into something more suitable for daily use.