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(' ', '')}")
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}
}
Runs of lightonai RITA_s on huggingface.co
1.7K
Total runs
0
24-hour runs
0
3-day runs
38
7-day runs
-2.4K
30-day runs
More Information About RITA_s huggingface.co Model
RITA_s huggingface.co
RITA_s huggingface.co is an AI model on huggingface.co that provides RITA_s's model effect (), which can be used instantly with this lightonai RITA_s model. huggingface.co supports a free trial of the RITA_s model, and also provides paid use of the RITA_s. Support call RITA_s model through api, including Node.js, Python, http.
RITA_s huggingface.co is an online trial and call api platform, which integrates RITA_s's modeling effects, including api services, and provides a free online trial of RITA_s, you can try RITA_s online for free by clicking the link below.
lightonai RITA_s online free url in huggingface.co:
RITA_s is an open source model from GitHub that offers a free installation service, and any user can find RITA_s on GitHub to install. At the same time, huggingface.co provides the effect of RITA_s install, users can directly use RITA_s installed effect in huggingface.co for debugging and trial. It also supports api for free installation.