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

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	---
	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](https://huggingface.co/McClain/plasmidgpt-addgene-gpt2) 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

	```python
	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

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ucl-cssb/PlasmidRL/runs/u3wt9c50)

	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](https://huggingface.co/papers/2402.03300).

	The reward function optimizes for:
	1. Presence of a valid origin of replication (ORI)
	2. Presence of a valid antibiotic resistance marker (AMR)
	3. 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

	```bibtex
	@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}}
	}
	```