from transformers import AutoModel, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_m, trust_remote_code=True")
tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_m")
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_m on huggingface.co
637
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3-day runs
145
7-day runs
616
30-day runs
More Information About RITA_m huggingface.co Model
RITA_m huggingface.co
RITA_m huggingface.co is an AI model on huggingface.co that provides RITA_m's model effect (), which can be used instantly with this lightonai RITA_m model. huggingface.co supports a free trial of the RITA_m model, and also provides paid use of the RITA_m. Support call RITA_m model through api, including Node.js, Python, http.
RITA_m huggingface.co is an online trial and call api platform, which integrates RITA_m's modeling effects, including api services, and provides a free online trial of RITA_m, you can try RITA_m online for free by clicking the link below.
lightonai RITA_m online free url in huggingface.co:
RITA_m is an open source model from GitHub that offers a free installation service, and any user can find RITA_m on GitHub to install. At the same time, huggingface.co provides the effect of RITA_m install, users can directly use RITA_m installed effect in huggingface.co for debugging and trial. It also supports api for free installation.