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 / model.safetensors

Commit History

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

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McClain commited on Jan 26

Refresh weights and simplify README; add W&B link (#2)

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McClain commited on Dec 3, 2025

Upload model.safetensors

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McClain commited on Nov 27, 2025

Upload 9 files

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McClain commited on Nov 13, 2025

Commit History

Update model to grpo-production-20251110 (90% pass rate, 3 ORI types at temp 1.3) db2462a verified

Refresh weights and simplify README; add W&B link (#2) c7cbbd2 verified

Upload model.safetensors db9e2c2 verified

Upload 9 files 280a94d verified

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

db2462a
verified

Refresh weights and simplify README; add W&B link (#2)

c7cbbd2
verified

Upload model.safetensors

db9e2c2
verified

Upload 9 files

280a94d
verified