RITA_l / README.md
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---
language: protein
tags:
- protein
datasets:
- uniref-100
---
# RITA-L
RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard.
Model | #Params | d_model | layers | lm loss uniref-100
--- | --- | --- | --- | --- |
[Small](https://huggingface.co/lightonai/RITA_s) | 85M | 768 | 12 | 2.31
[Medium](https://huggingface.co/lightonai/RITA_m) | 300M | 1024 | 24 | 2.01
[**Large**](https://huggingface.co/lightonai/RITA_l)| 680M | 1536 | 24 | 1.82
[XLarge](https://huggingface.co/lightonai/RITA_xl)| 1.2B | 2048 | 24 | 1.70
For full results see our preprint: https://arxiv.org/abs/2205.05789
## Usage
Instantiate a model like so:
``` python
from transformers import AutoModel, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_l, trust_remote_code=True")
tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_l")
```
for generation we support pipelines:
``` python
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(' ', '')}")
```
## How to cite
@article{hesslow2022rita,
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},
year={2022}
}