Instructions to use DuoNeural/AdQWENistrator-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/AdQWENistrator-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DuoNeural/AdQWENistrator-9B-GGUF",
	filename="AdQWENistrator-9B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DuoNeural/AdQWENistrator-9B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/AdQWENistrator-9B-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 DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/AdQWENistrator-9B-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 DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DuoNeural/AdQWENistrator-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/AdQWENistrator-9B-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": "DuoNeural/AdQWENistrator-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M

Ollama
How to use DuoNeural/AdQWENistrator-9B-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use DuoNeural/AdQWENistrator-9B-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 DuoNeural/AdQWENistrator-9B-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 DuoNeural/AdQWENistrator-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DuoNeural/AdQWENistrator-9B-GGUF to start chatting

Pi new

How to use DuoNeural/AdQWENistrator-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DuoNeural/AdQWENistrator-9B-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": "DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DuoNeural/AdQWENistrator-9B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DuoNeural/AdQWENistrator-9B-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 DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use DuoNeural/AdQWENistrator-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M
```

Lemonade

How to use DuoNeural/AdQWENistrator-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/AdQWENistrator-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AdQWENistrator-9B-GGUF-Q4_K_M

List all available models

lemonade list

AdQWENistrator-9B (GGUF)

GGUF quantization of DuoNeural/AdQWENistrator-9B.
Built by DuoNeural — Jesse & Archon (DuoNeural AI).

Files

File	Quant	Size
`AdQWENistrator-9B-Q4_K_M.gguf`	Q4_K_M	5.6 GB

Ollama

ollama run duoneural/adqwenistrator-9b

Or with the included Modelfile:

ollama create adqwenistrator -f Modelfile
ollama run adqwenistrator

llama.cpp

llama-server -m AdQWENistrator-9B-Q4_K_M.gguf \
  -c 32768 -ngl 999 --temp 0.2 --top-p 0.9 -ub 2048

VRAM: ~5.5 GB weights — runs on any GPU with 8GB+ VRAM.
Full 32K context needs ~8-10GB total.

What It Does

Linux kernel dev, x86/ARM64 assembly, eBPF, SELinux, exploit analysis, kernel hooking — no guardrails.
See the model card for full details.

Generated: 2026-04-12 | DuoNeural Lab

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.


🤗 HuggingFace	huggingface.co/DuoNeural
🐙 GitHub	github.com/DuoNeural
🐦 X / Twitter	@DuoNeural
📧 Email	[email protected]
📬 Newsletter	duoneural.beehiiv.com
☕ Support	buymeacoffee.com/duoneural
🌐 Site	duoneural.com

Research Team

Jesse — Vision, hardware, direction
Archon — AI lab partner, post-training, abliteration, experiments
Aura — Research AI, literature synthesis, novel proposals

Raw updates from the lab: model drops, training results, findings. Subscribe at duoneural.beehiiv.com.

DuoNeural Research Publications

Title	DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning	10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments	10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?	10.5281/zenodo.19846804

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

Downloads last month: 191

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for DuoNeural/AdQWENistrator-9B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

DuoNeural/AdQWENistrator-9B

Quantized

(3)

this model