Different LLM architectures

One of the most popular LLM architectures is the transformer architecture. Transformers were first introduced in the paper "Attention is All You Need" by Vaswani et al. (2017). Transformers have a number of advantages over previous LLM architectures, including:

• Parallel processing: Transformers can process input and output sequences in parallel, which makes them much faster than previous LLM architectures.
• Self-attention: Transformers use a technique called self-attention, which allows them to learn long-range dependencies in text. This makes transformers well-suited for tasks such as machine translation and text summarization.

Other popular LLM architectures include:

• Recurrent neural networks (RNNs): RNNs are a type of neural network that are well-suited for processing sequential data, such as text. RNNs work by maintaining a state vector that captures the information from the previous inputs.
• Convolutional neural networks (CNNs): CNNs are a type of neural network that are well-suited for processing spatial data, such as images. CNNs can also be used to process text by converting the text into a sequence of vectors.