  Inspired by recent work demonstrating the promise of smaller
Transformer-based language models pretrained on carefully curated data, we
supercharge such approaches by investing heavily in curating a novel, high
quality, non-synthetic data mixture based solely on evaluation benchmarks.
Using our novel dataset mixture consisting of less than 100 thousand tokens, we
pretrain a 1 million parameter transformer-based LLM \textbf{phi-CTNL}
(pronounced ``fictional") that achieves perfect results across diverse academic
benchmarks, strictly outperforming all known foundation models.
\textbf{phi-CTNL} also beats power-law scaling and exhibits a never-before-seen
grokking-like ability to accurately predict downstream evaluation benchmarks'
canaries.
