RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

Last update: Dec 22, 2022

Overview

RITA: a Study on Scaling Up Generative Protein Sequence Models

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

Results

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

Usage

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

@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}
}

RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

Related tags

Overview

RITA: a Study on Scaling Up Generative Protein Sequence Models

Results

Usage

How to cite

Owner

LightOn

Commonsense Ability Tests

A cool little repl-based simulation written in Python

Ranking Models in Unlabeled New Environments （iccv21）

PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020).

Official implementation of the network presented in the paper "M4Depth: A motion-based approach for monocular depth estimation on video sequences"

Virtual hand gesture mouse using a webcam

Python package for multiple object tracking research with focus on laboratory animals tracking.

PassAPI is a password generator in hash format and fully developed in Python, with the aim of teaching how to handle and build

[UNMAINTAINED] Automated machine learning for analytics & production

Machine Unlearning with SISA

Fiddle is a Python-first configuration library particularly well suited to ML applications.

Continual learning with sketched Jacobian approximations

MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)

Official pytorch implementation of "Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization" ACMMM 2021 (Oral)

ICON: Implicit Clothed humans Obtained from Normals

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

Official PyTorch implementation of the paper: Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting.

Automatic meme generation model using Tensorflow Keras.

The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

LRBoost is a scikit-learn compatible approach to performing linear residual based stacking/boosting.