Instructions to use UCL-CSSB/PlasmidGPT-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UCL-CSSB/PlasmidGPT-GRPO with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="UCL-CSSB/PlasmidGPT-GRPO")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT-GRPO")
model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT-GRPO", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UCL-CSSB/PlasmidGPT-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UCL-CSSB/PlasmidGPT-GRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UCL-CSSB/PlasmidGPT-GRPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/UCL-CSSB/PlasmidGPT-GRPO

SGLang

How to use UCL-CSSB/PlasmidGPT-GRPO with SGLang:

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-GRPO" \
    --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-GRPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use 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-GRPO" \
        --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-GRPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use UCL-CSSB/PlasmidGPT-GRPO with Docker Model Runner:
```
docker model run hf.co/UCL-CSSB/PlasmidGPT-GRPO
```

PlasmidGPT-GRPO / README.md

McClain

Update model to grpo-production-20251110 (90% pass rate, 3 ORI types at temp 1.3)

db2462a verified 6 months ago

3.36 kB

base_model: McClain/plasmidgpt-addgene-gpt2
library_name: transformers
model_name: PlasmidGPT-GRPO
tags:
  - generated_from_trainer
  - grpo
  - trl
  - biology
  - plasmid
  - dna
  - synthetic-biology
license: mit
datasets:
  - McClain/plasmids-ncbi-addgene
pipeline_tag: text-generation

PlasmidGPT-GRPO

A generative model for plasmid DNA sequences, fine-tuned with Group Relative Policy Optimization (GRPO) reinforcement learning.

Model Description

This model is a fine-tuned version of PlasmidGPT optimized using GRPO to generate valid, functional plasmid sequences with:

Origin of replication (ORI) - Required for plasmid maintenance
Antibiotic resistance marker (AMR) - Required for selection

Performance

At temperature 1.3, this model achieves:

90% QC pass rate (valid ORI + AMR)
3 unique ORI types (ColE1, Col(pHAD28), Col440I)
100% unique sequences (no duplicates)

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

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

# Generate a plasmid starting with ATG (start codon)
prompt = "ATG"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=2000,
    temperature=1.3,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

sequence = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(sequence)

Training

This model was trained with GRPO (Group Relative Policy Optimization), a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

The reward function optimizes for:

Presence of a valid origin of replication (ORI)
Presence of a valid antibiotic resistance marker (AMR)
Absence of long repetitive sequences

Framework Versions

TRL: 0.23.1
Transformers: 4.57.0
PyTorch: 2.8.0
Datasets: 4.1.1
Tokenizers: 0.22.1

Recommended Sampling Parameters

Temperature	Pass Rate	ORI Diversity	Notes
0.8	37%	1 type	Collapsed - avoid
0.95	63%	2 types	Conservative
1.15	76%	2 types	Balanced
1.3	90%	3 types	Recommended

Citation

@article{shao2024deepseekmath,
    title={{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author={Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
    year=2024,
    eprint={arXiv:2402.03300},
}

@misc{vonwerra2022trl,
    title={{TRL: Transformer Reinforcement Learning}},
    author={Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year=2020,
    journal={GitHub repository},
    publisher={GitHub},
    howpublished={\url{https://github.com/huggingface/trl}}
}