Model Details
- Developed by: Destin Biringanine
- Entreprise: Code, Growth Alive
- Base model:
unsloth/gemma-4-E2B-it-unsloth-bnb-4bit
- Fine-tuning: LoRA (PEFT)
- Framework: Unsloth
- License: Apache-2.0
Intended Use
This model is designed for enterprise decision-support tasks, including:
- Contract analysis
- Invoice understanding
- Procurement intelligence
- Supplier risk assessment
- Financial document analysis
- Business reporting
- Explainable recommendations
The model follows a structured reasoning process consisting of:
- Evidence
- Reasoning
- Confidence
- Business Impact
- Recommended Actions
Training
The adapter was fine-tuned using Unsloth on a structured business reasoning dataset.
The objective was not to teach new business knowledge, but to improve the model's ability to produce consistent, transparent, and explainable business reasoning.
Example
Supplier A has delayed deliveries for three consecutive months.
The defect rate increased by 18%.
The contract expires next month.
Should the contract be renewed?
Output
{
"decision": "Do not renew without corrective actions.",
"evidence": [
"Three consecutive delivery delays",
"18% increase in defect rate"
],
"reasoning": "Operational performance has consistently deteriorated, increasing supply-chain risk.",
"confidence": 0.95,
"business_impact": "High operational risk",
"recommended_actions": [
"Request a corrective action plan",
"Evaluate alternative suppliers",
"Conduct a supplier performance review"
]
}
Limitations
This model is intended for research and demonstration purposes.
It should be used as a decision-support assistant rather than an autonomous decision-maker and performs best when combined with an external knowledge base or retrieval system.
Acknowledgements
This project was developed for the Build with Google Gemma Hackathon using:
- Google Gemma
- Unsloth
- Hugging Face Transformers
- PEFT
- PyTorch