  The Languini Kitchen serves as both a research collective and codebase
designed to empower researchers with limited computational resources to
contribute meaningfully to the field of language modelling. We introduce an
experimental protocol that enables model comparisons based on equivalent
compute, measured in accelerator hours. The number of tokens on which a model
is trained is defined by the model's throughput and the chosen compute class.
Notably, this approach avoids constraints on critical hyperparameters which
affect total parameters or floating-point operations. For evaluation, we
pre-process an existing large, diverse, and high-quality dataset of books that
surpasses existing academic benchmarks in quality, diversity, and document
length. On it, we compare methods based on their empirical scaling trends which
are estimated through experiments at various levels of compute. This work also
provides two baseline models: a feed-forward model derived from the GPT-2
architecture and a recurrent model in the form of a novel LSTM with ten-fold
throughput. While the GPT baseline achieves better perplexity throughout all
our levels of compute, our LSTM baseline exhibits a predictable and more
favourable scaling law. This is due to the improved throughput and the need for
fewer training tokens to achieve the same decrease in test perplexity.
Extrapolating the scaling laws leads of both models results in an intersection
at roughly 50,000 accelerator hours. We hope this work can serve as the
foundation for meaningful and reproducible language modelling research.
