Instructions to use globalnebula/NM-GMED-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use globalnebula/NM-GMED-1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
model = PeftModel.from_pretrained(base_model, "globalnebula/NM-GMED-1")

Transformers

How to use globalnebula/NM-GMED-1 with Transformers:

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

pipe = pipeline("text-generation", model="globalnebula/NM-GMED-1")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("globalnebula/NM-GMED-1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use globalnebula/NM-GMED-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "globalnebula/NM-GMED-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "globalnebula/NM-GMED-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/globalnebula/NM-GMED-1

SGLang

How to use globalnebula/NM-GMED-1 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 "globalnebula/NM-GMED-1" \
    --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": "globalnebula/NM-GMED-1",
		"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 "globalnebula/NM-GMED-1" \
        --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": "globalnebula/NM-GMED-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use globalnebula/NM-GMED-1 with Docker Model Runner:
```
docker model run hf.co/globalnebula/NM-GMED-1
```

Model Card for NutriGNN — Drug–Food Pharmacological Mapping LLM

NutriGNN is a multimodal, safety-aware biomedical reasoning model fine-tuned on the Meta-Llama-3-8B base using Parameter-Efficient Fine-Tuning (PEFT) with LoRA adapters.
It functions as an explainable AI assistant for drug–food interaction analysis, personalized nutritional recommendation, and biomedical report generation, grounded in a heterogeneous knowledge graph and graph neural embeddings.

Model Details

Model Description

NutriGNN integrates Graph Neural Networks (GNNs) with a Large Language Model reasoning layer to align pharmaceutical drug effects with nutraceutical and food-derived compounds.
The system performs cross-domain embedding alignment, safety constraint filtering (allergies, diseases, contraindications), and natural-language explanation/recipe generation.

Developed by: S Kunal Achintya Reddy
Model Type: Biomedical reasoning and recommendation LLM
Languages: English
License: MIT
Fine-tuned from model: meta-llama/Meta-Llama-3-8B
Frameworks: PyTorch, PEFT, Hugging Face Transformers, PyTorch Geometric
Version: 1.0 (2026)

NutriGNN is designed as a decision-support system, not a clinical diagnostic tool.

Uses

Direct Use

NutriGNN can be used directly for:

Drug → food compound mapping and analog discovery
Personalized dietary recommendations with safety filters
Contraindication and side-effect explanation generation
Biomedical recipe generation and structured reports
Conversational health copilots and educational assistants

Downstream Use

Domain adaptation for clinical nutrition or pharmacy analytics
Integration into healthcare dashboards or wellness platforms
Embedding as an explainability layer in biomedical RAG systems

Out-of-Scope Use

Automated medical diagnosis or treatment decisions
Emergency or critical care decision-making
Processing personal health data without consent
Replacing licensed healthcare professionals

Bias, Risks, and Limitations

Outputs depend on the quality and coverage of biomedical datasets used during training.
The model may underperform in rare drug–food interactions or niche medical domains.
Not a certified medical advisor; recommendations require professional validation.
Language and regional dietary diversity may affect accuracy.

Recommendations

Users and developers should:

Treat NutriGNN as decision support, not authoritative medical advice.
Enable safety filters (allergy and contraindication modules) during deployment.
Periodically retrain or fine-tune with updated biomedical datasets.
Combine with human oversight in clinical or regulated environments.

Training Details

Training Data

NutriGNN was trained on a curated biomedical corpus composed of:

Public pharmacological datasets (drug–target relations)
Nutraceutical and food compound datasets
Biomedical literature summaries and ontology mappings
Synthetic reasoning dialogues and recipe generation prompts
Structured knowledge graph triples for cross-domain alignment

All data used was publicly available, synthetic, or anonymized.

Training Procedure

Preprocessing

Data normalized into structured triplets: context → biochemical reasoning → safe recommendation
Ontology mapping and duplicate removal
Contraindication and allergy tags injected as control tokens
Recipe and explanation prompts standardized into instruction format

Training Hyperparameters

Base model: Meta-Llama-3-8B
Fine-tuning: LoRA (r=32, alpha=16, dropout=0.05)
Batch size: 32–64
Learning rate: 2e-4
Optimizer: AdamW
Precision: bf16 mixed precision
Epochs: 4–6
Context length: 4096 tokens
Training hardware: NVIDIA A100 GPU (80GB)

Speeds, Sizes, Times

Trainable parameters: ~110M
Adapter checkpoint size: ~2–3 GB
Average training throughput: ~380 tokens/sec
Total training time: ~11.8 GPU hours
Estimated training cost: $21.63 USD
(Equivalent to ~11–12 hours on a single A100 GPU instance at ~$1.8–2.0/hr spot pricing)

Evaluation Metrics

Metric	Purpose
Precision@K	Food analog retrieval quality
AUROC / AUPRC	Side-effect and target prediction
Cosine Similarity Threshold	Pharmacological alignment validity
Acceptance Rate	Recipe biological coherence
Zero-Shot Accuracy	Generalization to unseen drugs
Human Evaluation	Interpretability and clarity

System Architecture

![System Architecture](System Architecture.png)

Ethical and Safety Considerations

NutriGNN includes safety-aware reasoning modules, but:

It does not replace medical professionals.
It should not be used for regulated clinical decisions without validation.
Developers should implement consent, logging, and explainability layers.

Intended Deployment Contexts

Research prototypes and academic studies
Educational healthcare tools
Nutritional planning assistants
Biomedical RAG or graph-augmented reasoning systems

Not recommended for emergency or high-risk medical scenarios.

Citation

If you use this model in academic work:

@misc{nutrignn2026, title={NutriGNN: Graph–Language Modeling for Drug–Food Bioactivity Alignment}, author={S. Kunal Achintya Reddy}, year={2026}, note={Hugging Face Model Repository} }

License

MIT License — free for research and commercial use with attribution.

Final Note

NutriGNN bridges computational pharmacology and nutritional intelligence by combining graph-based reasoning with generative language models, enabling safer and more interpretable biomedical decision-support systems.

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