Summary
In this chapter, we discussed the transformer, the model that revolutionized NLP and artificial intelligence. Today, all models that have commercial applications are derivatives of the transformer, as we learned in this chapter. Understanding how it works on a mechanistic level, and how the various parts (self-attention, embedding, tokenization, and so on) work together, allows us to understand the limitations of modern models. We saw how it works internally in a visual way, thus exploring the motive of modern artificial intelligence from multiple perspectives. Finally, we saw how we can adapt a transformer to our needs using techniques that leverage prior knowledge of the model. Now we can repurpose this process with virtually any dataset and any task.
Learning how to train a transformer will allow us to understand what happens when we take this process to scale. An LLM is a transformer with more parameters and that has been trained with more text. This leads to emergent...
