Instructions to use bochen2079/AORTA-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bochen2079/AORTA-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bochen2079/AORTA-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bochen2079/AORTA-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bochen2079/AORTA-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bochen2079/AORTA-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf bochen2079/AORTA-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bochen2079/AORTA-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf bochen2079/AORTA-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bochen2079/AORTA-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bochen2079/AORTA-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bochen2079/AORTA-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bochen2079/AORTA-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bochen2079/AORTA-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bochen2079/AORTA-7B-GGUF:Q4_K_M

Ollama
How to use bochen2079/AORTA-7B-GGUF with Ollama:
```
ollama run hf.co/bochen2079/AORTA-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use bochen2079/AORTA-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for bochen2079/AORTA-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for bochen2079/AORTA-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bochen2079/AORTA-7B-GGUF to start chatting

How to use bochen2079/AORTA-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bochen2079/AORTA-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bochen2079/AORTA-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use bochen2079/AORTA-7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bochen2079/AORTA-7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "bochen2079/AORTA-7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use bochen2079/AORTA-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/bochen2079/AORTA-7B-GGUF:Q4_K_M
```

Lemonade

How to use bochen2079/AORTA-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bochen2079/AORTA-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AORTA-7B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bochen2079/AORTA-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bochen2079/AORTA-7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default bochen2079/AORTA-7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

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:

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.

Model tree for bochen2079/AORTA-7B-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(381)

this model

bochen2079
/

AORTA-7B-GGUF

AORTA-7B-GGUF

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

Model Details

Quantizations

What AORTA Does

Training Categories

Usage

LM Studio

llama.cpp

Limitations

License

Links

Model tree for bochen2079/AORTA-7B-GGUF