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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Arkavo/sentinel 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 Arkavo/sentinel
# Run inference directly in the terminal:
llama cli -hf Arkavo/sentinel

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Arkavo/sentinel
# Run inference directly in the terminal:
llama cli -hf Arkavo/sentinel

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 Arkavo/sentinel
# Run inference directly in the terminal:
./llama-cli -hf Arkavo/sentinel

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 Arkavo/sentinel
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Arkavo/sentinel

Use Docker

docker model run hf.co/Arkavo/sentinel

LM Studio
Jan
Ollama
How to use Arkavo/sentinel with Ollama:
```
ollama run hf.co/Arkavo/sentinel
```
Unsloth Desktop

How to use Arkavo/sentinel with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Arkavo/sentinel

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Arkavo/sentinel"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Arkavo/sentinel with Docker Model Runner:
```
docker model run hf.co/Arkavo/sentinel
```

Lemonade

How to use Arkavo/sentinel with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Arkavo/sentinel

Run and chat with the model

lemonade run user.sentinel-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Arkavo/sentinel with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Arkavo/sentinel

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 Arkavo/sentinel

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Arkavo/sentinel with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Arkavo/sentinel

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 "Arkavo/sentinel" \
  --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"

arkavo-paul commited on 5 days ago

Commit

3821c3f

verified ·

1 Parent(s): 7011b6a

Eval calibration for the Northwind example

Browse files

Files changed (1) hide show

calibration.json +24 -0

calibration.json ADDED Viewed

	@@ -0,0 +1,24 @@

+{
+  "detector_version": "qwen3.5-0.8b-northwind-lora",
+  "taxonomy_version": "1.0.0",
+  "thresholds": {
+    "confidential": 1.0,
+    "internal": 0.5,
+    "public": 0.5
+  },
+  "example_only": true,
+  "eval": {
+    "rewrite": {
+      "n": 11,
+      "correct": 11
+    },
+    "unseen-rewrite": {
+      "n": 2,
+      "correct": 2
+    },
+    "unseen-verbatim": {
+      "n": 2,
+      "correct": 2
+    }
+  }
+}