This project trains a language model exclusively on datasets sourced from defined time periods rather than the full available corpus. The approach selectively filters training data chronologically to mitigate the influence of modern biases on model behavior. This methodology could enable researchers to study how historical context shapes model outputs or to produce responses more aligned with specific eras.