Exploring the Architecture of LLMs

 LLMs comprise several layers of neural networks similar to a human brain. While neurons are interconnected and send signals to each other in the human brain, it is the network nodes that are interconnected in neural networks.

They have several layers like the input layer, output layer, and some layers in between. Each layer can process data and provide outputs. Not all outputs are passed on to the next layer, but only those that fulfill a particular condition.

This helps the other layers to receive only the relevant information, which improves the efficiency of the model. 

Transformer Models

This is the most common architecture in LLMs and interacts similarly to humans. It allows the model to perform efficient text generation. 

There is also a notable feature in this model known as the attention mechanism. It allows the model to focus on various parts of a sentence to get a better understanding of the context. 

Let us consider an example to understand this model better. When you are watching a movie, though you will want to watch all the scenes, your focus will be more on important scenes that form the crux of the story. 

Similarly, these models focus on the most relevant parts of the text to understand the context and provide meaningful responses.

Know more - https://botpenguin.com/blogs/a-beginner-guide-to-llm-integration-for-ai-powered-systems