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UCL-CSSB
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Model Details
Model Provider
UCL-CSSB
Model Tree
UCL-CSSB/PlasmidGPT
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT-GRPO")tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT-GRPO") input_ids = tokenizer("ATG", return_tensors="pt").input_idsoutputs = model.generate(input_ids, max_new_tokens=512, do_sample=True, temperature=1.0)print(tokenizer.decode(outputs[0], skip_special_tokens=True))Recommended sampling: T=1.0 for direct generation, T=1.15 for rejection sampling (per the paper).
bibtex
@inproceedings{thiel2026plasmidrl, title = {Effects of Structural Reward Shaping on Biophysical Properties in {RL}-Trained Plasmid Generators}, author = {Thiel, McClain and Cunningham, Angus G. and Barnes, Chris P.}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, year = {2026}}