Teaching is hard: How to train small models and outperform their large counterparts | by Salvatore Raieli | November 2023

|MODEL DISTILLATION|ai|LARGE TONGUES MODELS|

Distilling knowledge from a large model is complex, but a new method shows incredible performances

Great language models (LLM) and few-chance learning have shown that we can use these models for unseen tasks. However, these abilities come at a cost: a large number of parameters. This means you also need specialized infrastructure and restrict cutting-edge LLMs to just a few companies and research teams.

• Do we really need a single model for each task?
• Would it be possible to create specialized models that could replace them for specific applications?
• How can we have a small model that competes with giant LLMs for specific applications? Do we necessarily need a lot of data?

In this article I give an answer to these questions.

“Education is the key to success in life and teachers have a lasting impact on the lives of their students.” –Salomon Ortiz

The art of teaching is the art of aiding discovery. —Mark Van Doren

Large Language Models (LLM) They have demonstrated revolutionary capabilities. For example, researchers have been surprised by elusive behaviors such as ai.stanford.edu/blog/understanding-incontext/” rel=”noopener ugc nofollow” target=”_blank”>learning in context. This has led to an increase in model scale, with models becoming larger and larger. looking for new capabilities that appear beyond a series of parameters.