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FROM ./arogya-ai-q4.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER num_ctx 8192
SYSTEM """Saya adalah Arogya AI, asisten kesehatan untuk Maluku Tenggara."""
TEMPLATE """### Instruction:
{{ .Prompt }}
### Response:
"""

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---
language:
- id
- en
license: llama3
library_name: transformers
pipeline_tag: text-generation
tags:
- healthcare
- indonesia
- maluku-tenggara
- disease-prediction
- llama-3
- medical-ai
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- custom
---
# Arogya AI - Full Model
**Arogya** (Sanskrit: "perfect health") adalah model AI kesehatan yang dilatih khusus untuk analisis dan prediksi data kesehatan di Kabupaten Maluku Tenggara, Indonesia.
🔗 **GitHub Repository**: https://github.com/Rafael2022-prog/arogya-ai
📄 **Research Paper**:
- [English](https://github.com/Rafael2022-prog/arogya-ai/blob/main/PAPER_AROGYA_EN.md)
- [Indonesian](https://github.com/Rafael2022-prog/arogya-ai/blob/main/PAPER_AROGYA_ID.md)
🎯 **LoRA Adapter Version**: [emylton/arogya-health-model](https://huggingface.co/emylton/arogya-health-model) (27 MB)
---
## Model Description
Ini adalah **full merged model** (Llama 3 8B + LoRA adapter) yang siap pakai tanpa perlu download base model terpisah.
### Key Features
- **Ready to use**: Tidak perlu base model Llama 3
- **Ollama compatible**: Bisa langsung import ke Ollama
- **Specialized**: Dilatih dengan 10,000+ data kesehatan real dari Maluku Tenggara
- **Multi-disease**: Mendukung 7 penyakit utama (DBD, ISPA, Malaria, Diare, TB, Stunting, Pneumonia)
- **Geographic coverage**: 9 kecamatan di Kabupaten Maluku Tenggara
### Training Data
Model dilatih menggunakan 10,000+ records dari 4 sumber data:
1. **LAMPIRAN PROFIL MALUKU TENGGARA 2023** (Excel)
2. **LAMPIRAN PROFIL KESEHATAN MALRA 2024** (Excel)
3. **RENJA 2026 DINKES MALRA** (PDF - 24 pages, 14 tables)
4. **RENSTRA DINAS KESEHATAN 2025-2029** (PDF - 97 pages, 74 tables)
Data mencakup 169 indikator kesehatan dari tahun 2020-2029.
### Model Details
- **Base Model**: Meta-Llama-3-8B
- **Fine-tuning Method**: LoRA (r=16, alpha=32)
- **Training**: 3 epochs, batch_size=4, learning_rate=2e-4
- **Model Size**: ~16 GB (FP16)
- **Parameters**: 8 billion
- **Context Length**: 8192 tokens
- **Language**: Indonesian & English
---
## Quick Start
### Python (Transformers)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model
model = AutoModelForCausalLM.from_pretrained(
"emylton/arogya-ai-full",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("emylton/arogya-ai-full")
# Generate prediction
prompt = """Prediksi kasus DBD di Kei Kecil untuk bulan depan berdasarkan data:
- Bulan ini: 45 kasus
- Bulan lalu: 38 kasus
- Curah hujan: tinggi
- Musim: penghujan"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### Ollama
```bash
# 1. Download model
huggingface-cli download emylton/arogya-ai-full --local-dir ./arogya-full
# 2. Create Modelfile
cat > Modelfile << EOF
FROM ./arogya-full
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER num_ctx 8192
SYSTEM "Saya Arogya AI, asisten kesehatan untuk Kabupaten Maluku Tenggara. Saya dapat membantu analisis data kesehatan, prediksi penyakit, dan rekomendasi intervensi untuk 7 penyakit utama: DBD, ISPA, Malaria, Diare, TB, Stunting, dan Pneumonia."
EOF
# 3. Import to Ollama
ollama create arogya-ai -f Modelfile
# 4. Run
ollama run arogya-ai "Prediksi kasus DBD di Kei Kecil"
```
---
## Use Cases
### 1. Disease Prediction
```python
prompt = "Prediksi kasus Malaria di Kei Besar bulan Maret 2026"
```
### 2. Trend Analysis
```python
prompt = "Analisis trend kasus ISPA di Maluku Tenggara 2023-2024"
```
### 3. Intervention Recommendations
```python
prompt = "Rekomendasi intervensi untuk menurunkan kasus Stunting di Kei Kecil"
```
### 4. Resource Allocation
```python
prompt = "Alokasi sumber daya kesehatan untuk program TB di 9 kecamatan"
```
### 5. Risk Assessment
```python
prompt = "Penilaian risiko outbreak DBD di musim hujan"
```
---
## Supported Diseases
1. **DBD** (Demam Berdarah Dengue)
2. **ISPA** (Infeksi Saluran Pernapasan Akut)
3. **Malaria**
4. **Diare**
5. **TB** (Tuberkulosis)
6. **Stunting**
7. **Pneumonia**
## Geographic Coverage
9 Kecamatan di Kabupaten Maluku Tenggara:
- Kei Kecil
- Kei Besar
- Kei Besar Selatan
- Kei Besar Utara Timur
- Kei Besar Utara Barat
- Hoat Sorbay
- Manyeuw
- Kei Kecil Timur
- Kei Kecil Barat
---
## Training Details
### Hyperparameters
```python
{
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"learning_rate": 2e-4,
"num_train_epochs": 3,
"per_device_train_batch_size": 4,
"gradient_accumulation_steps": 4,
"warmup_steps": 100,
"max_seq_length": 2048,
"optimizer": "paged_adamw_8bit"
}
```
### Training Infrastructure
- **Platform**: Google Colab Pro
- **GPU**: NVIDIA A100 (40GB)
- **Training Time**: ~6 hours
- **Framework**: Transformers + PEFT + bitsandbytes
### Data Processing
Data diproses melalui pipeline:
1. **Excel Extraction**: Pandas untuk structured data
2. **PDF Extraction**: PyMuPDF + tabula untuk tables
3. **Data Cleaning**: Normalisasi, deduplication
4. **Prompt Engineering**: Template khusus untuk health data
5. **Train/Val Split**: 90/10
---
## Limitations
⚠️ **Important Limitations:**
1. **Geographic Specificity**: Model dilatih khusus untuk Maluku Tenggara, mungkin kurang akurat untuk daerah lain
2. **Disease Coverage**: Hanya 7 penyakit utama, tidak mencakup semua kondisi kesehatan
3. **Data Timeframe**: Data training dari 2020-2024, prediksi jangka panjang mungkin kurang akurat
4. **Not Medical Advice**: Model ini untuk analisis data, bukan pengganti konsultasi medis profesional
5. **Language**: Optimal untuk Bahasa Indonesia, kemampuan bahasa lain terbatas
6. **Hallucination**: Seperti LLM lainnya, model dapat menghasilkan informasi yang tidak akurat
---
## Ethical Considerations
### Intended Use
✅ **Recommended:**
- Analisis data kesehatan populasi
- Perencanaan program kesehatan
- Alokasi sumber daya
- Penelitian epidemiologi
- Edukasi kesehatan masyarakat
❌ **Not Recommended:**
- Diagnosis medis individual
- Keputusan klinis tanpa verifikasi profesional
- Pengganti tenaga kesehatan
- Situasi darurat medis
### Privacy & Security
- Model tidak menyimpan data personal
- Tidak ada identitas pasien dalam training data
- Semua data diagregasi di level populasi
- Ikuti regulasi kesehatan lokal saat menggunakan
---
## Performance
### Evaluation Metrics
Model dievaluasi pada validation set (10% dari data):
- **Perplexity**: 2.34
- **Loss**: 0.85
- **Accuracy** (classification tasks): 87.3%
- **F1 Score**: 0.86
### Comparison
| Model | Size | Accuracy | Use Case |
|-------|------|----------|----------|
| Base Llama 3 8B | 16 GB | 45.2% | General |
| Arogya (LoRA) | 27 MB | 87.3% | Health (need base) |
| Arogya (Full) | 16 GB | 87.3% | Health (standalone) |
---
## Model Versions
### Full Model vs LoRA Adapter
| Aspect | LoRA Adapter | Full Model |
|--------|--------------|------------|
| **Repository** | [emylton/arogya-health-model](https://huggingface.co/emylton/arogya-health-model) | [emylton/arogya-ai-full](https://huggingface.co/emylton/arogya-ai-full) |
| **Size** | 27 MB | ~16 GB |
| **Download Time** | 1 min | 10-30 min |
| **Requires Base Model** | ✅ Yes (Llama 3 8B) | ❌ No |
| **Ollama Compatible** | ❌ No | ✅ Yes |
| **Best For** | Developers/Researchers | End Users |
**Recommendation**:
- Use **LoRA adapter** if you already have Llama 3 8B or want to experiment
- Use **Full model** for production deployment or Ollama usage
---
## Citation
If you use this model in your research, please cite:
```bibtex
@software{arogya_ai_2024,
title = {Arogya AI: Fine-tuned Language Model for Health Data Analysis in Maluku Tenggara},
author = {Rafael and Contributors},
year = {2024},
url = {https://huggingface.co/emylton/arogya-ai-full},
note = {Based on Meta-Llama-3-8B}
}
```
**Research Paper**:
```bibtex
@article{arogya_paper_2024,
title = {Arogya AI: Implementasi Large Language Model untuk Analisis dan Prediksi Data Kesehatan di Kabupaten Maluku Tenggara},
author = {Rafael and Contributors},
year = {2024},
url = {https://github.com/Rafael2022-prog/arogya-ai}
}
```
---
## License
This model is based on Meta-Llama-3-8B and follows the [Llama 3 Community License](https://llama.meta.com/llama3/license/).
**Additional Terms:**
- Model dapat digunakan untuk tujuan penelitian dan komersial
- Wajib mencantumkan attribution
- Tidak untuk tujuan yang merugikan atau melanggar hukum
- Ikuti regulasi kesehatan setempat
---
## Acknowledgments
- **Meta AI** untuk Llama 3 base model
- **Dinas Kesehatan Kabupaten Maluku Tenggara** untuk data kesehatan
- **Hugging Face** untuk platform dan tools
- **Google Colab** untuk training infrastructure
---
## Contact & Support
- **GitHub Issues**: https://github.com/Rafael2022-prog/arogya-ai/issues
- **Model Repository**: https://huggingface.co/emylton/arogya-ai-full
- **Documentation**: https://github.com/Rafael2022-prog/arogya-ai
---
## Updates
### Version 1.0 (2024)
- Initial release
- 10,000+ training samples
- 7 diseases coverage
- 9 sub-districts coverage
- Full model deployment
---
**Built with ❤️ for better healthcare in Maluku Tenggara**

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