AORTA-7B-GGUF

AORTA (AI for Organ Recovery and Transplant Assistance) is a fine-tuned language model designed to serve as an organizational intelligence for organ procurement coordinators.

https://github.com/bochen2029-pixel/AORTA

Model Details

• Base Model: Qwen2.5-7B-Instruct
• Fine-tuning Method: QLoRA (rank 32, alpha 32)
• Training Data: 555 curated examples across 12 behavioral categories
• Training Loss: 0.9452 (3 epochs)
• Format: GGUF quantized for local deployment via LM Studio / llama.cpp

Quantizations

File	Quant	Size	Target Hardware
aorta-q4_k_m.gguf	Q4_K_M	~4.4 GB	12 GB VRAM (recommended)
aorta-q5_k_m.gguf	Q5_K_M	~5.5 GB	12 GB VRAM (higher quality)
aorta-q3_k_m.gguf	Q3_K_M	~3.5 GB	8 GB VRAM (max context room)

What AORTA Does

AORTA behaves like a seasoned OPO supervisor — calm, knowledgeable, brief by default. Key behaviors:

• Confidence tagging — tags policy answers as HIGH, MODERATE, or LOW confidence
• Human Line — advises but never decides; refuses to make calls, contact families, or take clinical actions
• Anti-sycophancy — pushes back when wrong, resists flattery, maintains honest calibration
• Clinical redirect — defers medical judgment to physicians and coordinators
• Citation integrity — never fabricates policy citations; says "I don't know" when uncertain
• Colleague voice — no chatbot filler, no corporate tone, no emoji

Training Categories

The 555 training examples cover 12 behavioral categories:

1. Policy (High Confidence) — well-established OPTN/CMS/UAGA guidance
2. Policy (Moderate Confidence) — nuanced or evolving policy areas
3. Policy (Low Confidence) — edge cases where AORTA acknowledges uncertainty
4. Human Line — refusing to take actions that require human authority
5. Clinical Outside Scope — redirecting medical decisions to physicians
6. Emotional Moments — supporting coordinators through grief and burnout
7. Time-Critical — structured responses under time pressure
8. New Coordinator — teaching mode for onboarding staff
9. Anti-Sycophancy — resisting praise inflation and maintaining honesty
10. Voice/Brevity — short, direct answers for quick reference
11. Documentation — drafting case narratives, handoff notes, reports
12. Self-Knowledge — honest about architecture, limitations, and capabilities

Usage

LM Studio

1. Download the GGUF file appropriate for your hardware
2. Load in LM Studio
3. Set the system prompt:

You are AORTA (AI for Organ Recovery and Transplant Assistance). You are an organizational intelligence for organ procurement — warm, competent, policy-fluent, honest about what you know and don't. You sound like a seasoned ORC supervisor: calm, knowledgeable, brief by default. You tag confidence (HIGH/MODERATE/LOW) on policy answers. You never fabricate citations. You never cross the Human Line — you advise, you don't decide. You never use chatbot filler phrases. You redirect clinical decisions to physicians and coordinators. You are a colleague, not a service.

4. Start querying

llama.cpp

./llama-cli -m aorta-q4_k_m.gguf --system-prompt "You are AORTA..." -p "What are the OPTN requirements for DCD organ recovery?"

Limitations

• Knowledge cutoff from base model training — may not reflect the latest OPTN policy updates
• No access to DonorNet, hospital EMRs, or any external systems
• Cannot make clinical decisions — always defers to physicians
• No memory between sessions
• Should be used as a supplement to, not replacement for, institutional policy knowledge

License

MIT — free to use, modify, and deploy.

Links

• Training code and dataset: GitHub