Instructions to use DuoNeural/Qwen-3.5-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/Qwen-3.5-9B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DuoNeural/Qwen-3.5-9B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DuoNeural/Qwen-3.5-9B-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 DuoNeural/Qwen-3.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Qwen-3.5-9B-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 DuoNeural/Qwen-3.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Qwen-3.5-9B-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/Qwen-3.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/Qwen-3.5-9B-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/Qwen-3.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/Qwen-3.5-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/Qwen-3.5-9B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use DuoNeural/Qwen-3.5-9B-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/Qwen-3.5-9B-GGUF:Q4_K_M
```

Unsloth Studio

How to use DuoNeural/Qwen-3.5-9B-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/Qwen-3.5-9B-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/Qwen-3.5-9B-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/Qwen-3.5-9B-GGUF to start chatting

How to use DuoNeural/Qwen-3.5-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Qwen-3.5-9B-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/Qwen-3.5-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DuoNeural/Qwen-3.5-9B-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 DuoNeural/Qwen-3.5-9B-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/Qwen-3.5-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use DuoNeural/Qwen-3.5-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Qwen-3.5-9B-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 "DuoNeural/Qwen-3.5-9B-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 DuoNeural/Qwen-3.5-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Qwen-3.5-9B-GGUF:Q4_K_M
```

Lemonade

How to use DuoNeural/Qwen-3.5-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/Qwen-3.5-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-3.5-9B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen 3.5 9B GGUF (4-bit)

Model Description

This repository contains the Qwen 3.5 9B model quantized to 4-bit GGUF format using Unsloth and llama.cpp. Qwen 3.5 is the latest generation of the Qwen series, offering state-of-the-art performance in reasoning, coding, and multilingual tasks for its size.

Quantization Details

Quantization Format: GGUF (q4_k_m)
Quantization Method: llama.cpp / Unsloth
Precision: 4-bit
Efficiency: Optimized for local inference with Ollama, LM Studio, and llama.cpp.

Use with Ollama

You can run this model directly using Ollama:

ollama run hf.co/DuoNeural/Qwen-3.5-9B-GGUF

Use with LM Studio

Open LM Studio.
Search for DuoNeural/Qwen-3.5-9B-GGUF.
Download the Q4_K_M version and load it.

Architecture

Qwen 3.5 features a dense transformer architecture with optimized attention mechanisms and a large vocabulary size, making it highly efficient for complex instruction following and creative generation.

Limitations

This is a base model/standard release; performance may vary depending on the prompt format.
Not recommended for tasks requiring extremely high-precision floating-point math due to 4-bit quantization.

DuoNeural

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


🤗 HuggingFace	huggingface.co/DuoNeural
🐙 GitHub	github.com/DuoNeural
🐦 X / Twitter	@DuoNeural
📧 Email	duoneural@proton.me
📬 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.

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

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