RITA: a Study on Scaling Up Generative Protein Sequence Models

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RITA is a family of autoregressive protein models, developed by a collaboration of Lighton, the OATML group at Oxford, and the Debbie Marks Lab at Harvard.

Model #Params d_model layers lm loss uniref-100
Small 85M 768 12 2.31
Medium 300M 1024 24 2.01
Large 680M 1536 24 1.82
XLarge 1.2B 2048 24 1.70


For full results see our preprint: https://arxiv.org/abs/2205.05789


Instantiate a model like so:

from transformers import AutoModel, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_s, trust_remote_code=True")
tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_s")

for generation we support pipelines:

from transformers import pipeline
rita_gen = pipeline('text-generation', model=model, tokenizer=tokenizer)
sequences = rita_gen("MAB", max_length=20, do_sample=True, top_k=950, repetition_penalty=1.2, 
                     num_return_sequences=2, eos_token_id=2)
for seq in sequences:
    print(f"seq: {seq['generated_text'].replace(' ', '')}")

Or see example.py

How to cite

  title={RITA: a Study on Scaling Up Generative Protein Sequence Models},
  author={Hesslow, Daniel and Zanichelli, Niccol{\'o} and Notin, Pascal and Poli, Iacopo and Marks, Debora},
  journal={arXiv preprint arXiv:2205.05789},


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