Instructions to use glyphsoftware/sentinel-r2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use glyphsoftware/sentinel-r2-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="glyphsoftware/sentinel-r2-gguf",
	filename="sentinel-r2-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf glyphsoftware/sentinel-r2-gguf:Q4_K_M

Use Docker

docker model run hf.co/glyphsoftware/sentinel-r2-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use glyphsoftware/sentinel-r2-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "glyphsoftware/sentinel-r2-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": "glyphsoftware/sentinel-r2-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/glyphsoftware/sentinel-r2-gguf:Q4_K_M

Ollama
How to use glyphsoftware/sentinel-r2-gguf with Ollama:
```
ollama run hf.co/glyphsoftware/sentinel-r2-gguf:Q4_K_M
```

Unsloth Studio

How to use glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for glyphsoftware/sentinel-r2-gguf to start chatting

How to use glyphsoftware/sentinel-r2-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf glyphsoftware/sentinel-r2-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": "glyphsoftware/sentinel-r2-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use glyphsoftware/sentinel-r2-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf glyphsoftware/sentinel-r2-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 "glyphsoftware/sentinel-r2-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 glyphsoftware/sentinel-r2-gguf with Docker Model Runner:
```
docker model run hf.co/glyphsoftware/sentinel-r2-gguf:Q4_K_M
```

Lemonade

How to use glyphsoftware/sentinel-r2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull glyphsoftware/sentinel-r2-gguf:Q4_K_M

Run and chat with the model

lemonade run user.sentinel-r2-gguf-Q4_K_M

List all available models

lemonade list

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

This model is the proprietary property of Glyph Software LLP. Access is granted only to authorized licensees under a signed agreement. This is an offensive-security agent adapter intended solely for authorized penetration testing and security research. By requesting access you confirm you are an authorized user, that you will only use it against systems you are explicitly permitted to test, and that you agree to the terms in the LICENSE file.

Sentinel-R2 (GGUF)

Proprietary & Confidential. Sentinel-R2 is the exclusive property of Glyph Software LLP. It is not open source and is distributed under a proprietary, all-rights-reserved license.

GGUF builds of Sentinel-R2, an offensive-security agent for authorized penetration testing, for use with llama.cpp and compatible runtimes (Ollama, LM Studio, etc.). These are the full merged weights converted from the fine-tuned model — not the LoRA adapter.

Base model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M (architecture qwen3_5, a vision-capable multimodal model).

Files

File	Quant	Size	Notes
`sentinel-r2-bf16.gguf`	BF16	~17.9 GB	Full-precision source; use to derive further quants
`sentinel-r2-Q8_0.gguf`	Q8_0	~9.5 GB	Near-lossless
`sentinel-r2-Q4_K_M.gguf`	Q4_K_M	~5.6 GB	Recommended balance of size/quality
`sentinel-r2-mmproj-f16.gguf`	F16	~0.9 GB	Vision projector (multimodal input)

The multi-token-prediction (MTP) head from the base model is not included (converted with --no-mtp); it is only used for speculative drafting and is not required for normal inference.

Usage

Text generation:

llama-completion -m sentinel-r2-Q4_K_M.gguf -p "..." --temp 0.6

With vision (pass the projector):

llama-mtmd-cli -m sentinel-r2-Q4_K_M.gguf \
  --mmproj sentinel-r2-mmproj-f16.gguf \
  --image target.png -p "Describe this screenshot."

Sentinel-R2 is a reasoning + tool-use model: it emits <think> reasoning before its response. Provide it a target scope and a shell-execute tool for agentic use.

License

Downloads last month: -

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for glyphsoftware/sentinel-r2-gguf

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

empero-ai/Qwythos-9B-Claude-Mythos-5-1M

Quantized

(97)

this model