Let's dive deeper into the process of building a Small Language Model (SLM) using a Large Language Model (LLM) and explore the key components, benefits, and challenges involved.

Pruning

Pruning is a key step in reducing the size and computational requirements of a model. It involves removing unnecessary parameters, weights, or other components that are not essential for the task at hand. There are several techniques used for pruning, including:

1. Weight pruning: Removing weights that have a low magnitude or are not essential for the task.
2. Layer pruning: Removing entire layers or sub-layers that are not essential for the task.
3. Neuron pruning: Removing neurons that are not essential for the task.
4. Synaptic pruning: Removing synaptic connections that are not essential for the task.