Instructions to use QuantumindSSI/09-drug-interaction-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantumindSSI/09-drug-interaction-predictor with Transformers:

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

pipe = pipeline("text-generation", model="QuantumindSSI/09-drug-interaction-predictor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantumindSSI/09-drug-interaction-predictor", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantumindSSI/09-drug-interaction-predictor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantumindSSI/09-drug-interaction-predictor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantumindSSI/09-drug-interaction-predictor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantumindSSI/09-drug-interaction-predictor

SGLang

How to use QuantumindSSI/09-drug-interaction-predictor 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 "QuantumindSSI/09-drug-interaction-predictor" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantumindSSI/09-drug-interaction-predictor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "QuantumindSSI/09-drug-interaction-predictor" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantumindSSI/09-drug-interaction-predictor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantumindSSI/09-drug-interaction-predictor with Docker Model Runner:
```
docker model run hf.co/QuantumindSSI/09-drug-interaction-predictor
```

Drug Interaction Predictor

A fine-tuned Small Language Model (SLM) that predicts potential drug-drug interactions from medication lists, including severity, mechanism, evidence, and clinical recommendations.

Model Details

Attribute	Value
Developer	QuantumIndSSI Ltd
Base Model	./base_model
Architecture	Transformer decoder (causal LM)
Fine-tuning Method	LoRA (Low-Rank Adaptation)
LoRA Rank	16
LoRA Alpha	32
License	apache-2.0

Intended Use

Automated drug-drug interaction screening from medication lists
Clinical decision support for pharmacists and prescribers
Patient safety and pharmacovigilance workflows
Edge deployment on Victron and other constrained hardware

Training Data

100,000+ synthetic drug-drug interaction examples
Drug classes: anticoagulants, statins, antibiotics, antidepressants, PPIs, NSAIDs, opioids, immunosuppressants, and more
Interaction types: pharmacokinetic (CYP inhibition/induction, P-gp), pharmacodynamic (additive toxicity, QT prolongation, bleeding)
Severity levels: Critical, High, Medium, Low

Usage

model_id = "QuantumindSSI/09_drug_interaction_predictor"
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = """Medications: Warfarin, Aspirin, Omeprazole. Analyze for interactions."""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Not a substitute for clinical pharmacologist review
Synthetic training data may not capture all real-world interactions
English only
Always verify with FDA labels, clinical guidelines, and drug databases

Hardware Requirements

Target	RAM	Notes
Cloud GPU	4GB	FP16 inference
Workstation	3GB	INT8 quantized
Victron Edge	2-3GB	INT8/INT4 quantized, CPU

Citation

@misc{09_drug_interaction_predictor,
  title={Drug Interaction Predictor},
  author={QuantumIndSSI Ltd},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/QuantumindSSI/09_drug_interaction_predictor}}
}

Contact

GitHub: https://github.com/QuantumindSSI
HuggingFace: https://huggingface.co/QuantumindSSI

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