---
license: mit
library_name: transformers
pipeline_tag: text-generation
base_model: UCL-CSSB/PlasmidGPT
tags:
- biology
- plasmid
- dna
- synthetic-biology
- gpt2
---

# PlasmidGPT-SFT

Supervised fine-tune of [PlasmidGPT](https://huggingface.co/UCL-CSSB/PlasmidGPT) on a curated corpus of ~15k engineered *E. coli* plasmids from PlasmidScope and Addgene (Cunningham et al., 2025). Used as a baseline for the GRPO-trained [PlasmidGPT-GRPO](https://huggingface.co/UCL-CSSB/PlasmidGPT-GRPO).

## Quick start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT-SFT")
tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT-SFT")

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

```bibtex
@article{cunningham2025plasmidsft,
  title   = {Generative design and construction of functional plasmids with a {DNA} language model},
  author  = {Cunningham, Angus G. and Dekker, Linda and Shcherbakova, Anastasiia and Barnes, Chris P.},
  journal = {bioRxiv},
  year    = {2025},
  doi     = {10.64898/2025.12.06.692736}
}
```