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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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

(87)

this model