What does it mean to distill a machine learning model or LLM?

Distillation is a technique used in machine learning to create smaller, faster, and more efficient versions of neural networks while retaining most of their performance. This process involves two key players: a large teacher model and a smaller student model. The teacher model generates soft labels, which are richer than basic one-hot labels, providing information about the confidence distribution. The student model learns to replicate these distributions, gaining much of the teacher's knowledge in the process. Distillation leverages logits from the larger teacher model to train the smaller student model, transferring knowledge more effectively than using hard labels alone. A code example is provided to demonstrate this process, but in real-world scenarios, a much larger dataset and careful experimentation with hyperparameters are required. Despite its benefits, distillation has limitations and requires ongoing monitoring and maintenance to prevent issues such as model drift and biases.

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