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.

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:

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

Usage

LM Studio

Download the GGUF file appropriate for your hardware
Load in LM Studio
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.

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.

bochen2079/AORTA-7B-GGUF

AORTA-7B-GGUF