Instructions to use thodsapon/qwen3.5-9b-fenrir-data-security with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use thodsapon/qwen3.5-9b-fenrir-data-security 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 thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thodsapon/qwen3.5-9b-fenrir-data-security: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 thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf thodsapon/qwen3.5-9b-fenrir-data-security: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 thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M

Use Docker

docker model run hf.co/thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M

LM Studio
Jan

vLLM

How to use thodsapon/qwen3.5-9b-fenrir-data-security with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thodsapon/qwen3.5-9b-fenrir-data-security"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thodsapon/qwen3.5-9b-fenrir-data-security",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M

Ollama
How to use thodsapon/qwen3.5-9b-fenrir-data-security with Ollama:
```
ollama run hf.co/thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M
```

Unsloth Studio

How to use thodsapon/qwen3.5-9b-fenrir-data-security 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 thodsapon/qwen3.5-9b-fenrir-data-security 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 thodsapon/qwen3.5-9b-fenrir-data-security to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thodsapon/qwen3.5-9b-fenrir-data-security to start chatting

How to use thodsapon/qwen3.5-9b-fenrir-data-security with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thodsapon/qwen3.5-9b-fenrir-data-security: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": "thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use thodsapon/qwen3.5-9b-fenrir-data-security with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thodsapon/qwen3.5-9b-fenrir-data-security: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 "thodsapon/qwen3.5-9b-fenrir-data-security: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 thodsapon/qwen3.5-9b-fenrir-data-security with Docker Model Runner:
```
docker model run hf.co/thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M
```

Lemonade

How to use thodsapon/qwen3.5-9b-fenrir-data-security with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M

Run and chat with the model

lemonade run user.qwen3.5-9b-fenrir-data-security-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use thodsapon/qwen3.5-9b-fenrir-data-security with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thodsapon/qwen3.5-9b-fenrir-data-security: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 thodsapon/qwen3.5-9b-fenrir-data-security:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.5 9B Fenrir Data Security - GGUF Q4/Q6

GGUF exports of a Qwen3.5 9B model fine-tuned for defensive cybersecurity and data-security tasks.

Dataset reference used for fine-tuning: https://huggingface.co/datasets/AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1

Files

File	Size	SHA256
`fenrir-qwen3.5-9b-final-adapter-Q4_K_M.gguf`	5.63 GB	`ec1331aa7efe5299f3fded0175707b4d8c3341b81d8f63a1e094a4e7123733b7`
`fenrir-qwen3.5-9b-final-adapter-Q6_K.gguf`	7.36 GB	`789df5ef6ea3581eb0551d7d88ce22c62ce88bfd2be0aaf6f5b55e278480d114`

Use

llama-cli -m fenrir-qwen3.5-9b-final-adapter-Q4_K_M.gguf \
  -p "Explain a defensive threat model for a file upload feature." \
  -n 512

Colab notebook for testing both Q4 and Q6: fenrir_qwen35_gguf_q4_q6_colab.ipynb

For serving, use a Qwen-compatible chat template and tune context length, GPU layers, batch size, and sampling for your hardware.

Fine-Tune Setup

Item	Value
Base	local Qwen3.5 9B checkpoint
Dataset	Cybersecurity-Dataset-Fenrir-v2.1
Rows after filtering	99,403
Split	97,403 train / 1,000 eval / 1,000 validation / 1,000 test
Method	non-thinking SFT, response-only loss
Max sequence length	4,096
Epochs / steps	2.0 / 12,176
Effective batch	16
LR / scheduler	2e-4 / linear
LoRA	r=32, alpha=32, dropout=0.0
Target modules	q/k/v/o/gate/up/down projections

Results

Metric	Value
Train loss	0.04125
Eval loss	0.74160
Test loss	2.38501

Local evaluation summary:

Evaluation	Base	Fine-tuned	Note
General cyber prompts, n=20	87.5/100	78.0/100	Base was stronger on concise general answers.
Held-out Fenrir rows, n=12	47.7/60	50.6/60	Fine-tuned model aligned better with Fenrir-style content.

GGUF Notes

Architecture: qwen35
Quantization: Q4_K_M, Q6_K
Context metadata: 262,144
Exported with llama.cpp --no-mtp

Scope

Intended for defensive cybersecurity, data-security analysis, hardening guidance, and security-control explanation. This is a quantized GGUF artifact only, not the original LoRA adapter or full training checkpoint. Run your own safety and task-specific tests before production use.

Downloads last month: 18

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

6-bit

thodsapon
/

qwen3.5-9b-fenrir-data-security

Qwen3.5 9B Fenrir Data Security - GGUF Q4/Q6

Files

Use

Fine-Tune Setup

Results

GGUF Notes

Scope

Dataset used to train thodsapon/qwen3.5-9b-fenrir-data-security