PlasmidRL
Collection
Collection of models for ICML 2026 paper Effects of Structural Reward Shaping on Biophysical Properties in RL-Trained Plasmid Generators. • 3 items • Updated
Text Generation
Transformers
Safetensors
gpt2
biology
plasmid
dna
synthetic-biology
text-generation-inference
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "UCL-CSSB/PlasmidGPT" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "UCL-CSSB/PlasmidGPT",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
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}
}
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "UCL-CSSB/PlasmidGPT" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UCL-CSSB/PlasmidGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'