  In this work we introduce RITA: a suite of autoregressive generative models
for protein sequences, with up to 1.2 billion parameters, trained on over 280
million protein sequences belonging to the UniRef-100 database. Such generative
models hold the promise of greatly accelerating protein design. We conduct the
first systematic study of how capabilities evolve with model size for
autoregressive transformers in the protein domain: we evaluate RITA models in
next amino acid prediction, zero-shot fitness, and enzyme function prediction,
showing benefits from increased scale. We release the RITA models openly, to
the benefit of the research community.
