A stripped-down variant of Llama 2 that experiments with a reduced number of key-value attention heads, which can lower memory usage and speed up inference at the potential cost of some model capacity. It behaves like a standard text-in, text-out language model but with architectural tweaks that make it interesting for researchers exploring attention head efficiency. Details about how this modification affects real-world output quality are limited beyond the structural change itself.