metadata
license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
- biology
- plasmid
- dna
- synthetic-biology
- gpt2
PlasmidGPT
A HuggingFace-compatible repackaging of PlasmidGPT (Shao, 2024) — a GPT-2-style decoder pretrained on 153k engineered plasmid sequences from Addgene. Loadable with standard AutoModelForCausalLM and AutoTokenizer. Used as the base for PlasmidGPT-SFT and PlasmidGPT-GRPO.
Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT")
tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT")
input_ids = tokenizer("ATG", return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_new_tokens=512, do_sample=True, temperature=1.0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Citation
@article{shao2024plasmidgpt,
title = {{PlasmidGPT}: a generative framework for plasmid design and annotation},
author = {Shao, Bin},
journal = {bioRxiv},
year = {2024},
doi = {10.1101/2024.09.30.615762}
}