AI Breakdown or: I Read the Entire 78-page Llama-2 Paper So You Don’t Have To

The Llama-2 paper presents a collection of four generative AI models with varying parameters, built on a classic Transformer Architecture. These models employ RMSNorm for easier handling of large token numbers and weights, SwiGLU activation function to determine neuron activity, and RoPE method to emphasize the importance of word positions in sentences. Llama-2 also has a chatbot version called "Llama-2-Chat" that uses Ghost Attention (GAtt) for improved dialogue flow over multiple turns. Comparisons show Llama-2 outperforming its predecessor, Llama-1, and other open-source models in various benchmarks. However, it does not beat closed-source models like GPT-4 and PaLM-2-L. Meta did not use any user data from their platforms for training Llama-2 and has offset all carbon emissions generated during its development.