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
```

09-drug-interaction-predictor / README.md

QuantumindSSI

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- quantumindssi
	- sovereign-ai
	- edge-computing
	- healthcare-medical-ai
	- drug-interactions
	- pharmacovigilance
	- ddi
	- patient-safety
	- 09_drug_interaction_predictor
	- finetuned
	- lora
	inference: true
	---

	# 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

	```python
	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

	```bibtex
	@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