LLMs landscape, progression, and expansion
We can write many chapters on how modern LLMs have leveraged transformer model architecture, along with its explosive expansion and the numerous models being created on almost on a daily basis. However, in this last section, let’s distill the usage of LLMs and their progression thus far and also add an exciting new layer of additional expansion to the functionality of LLMs using AutoGen.
Exploring the landscape of transformer architectures
With their ability to handle a myriad of tasks, transformer models have revolutionized the field of natural language processing. By tweaking their architecture, we can create different types of transformer models, each with its unique applications. Let’s delve into three prevalent types:
- Models with encoders only: These models, equipped solely with an encoder, are typically employed for tasks that involve understanding the context of the input, such as text classification, sentiment...
