Instructions to use OpenAdminOS/openadmin-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OpenAdminOS/openadmin-8b 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 OpenAdminOS/openadmin-8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OpenAdminOS/openadmin-8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OpenAdminOS/openadmin-8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OpenAdminOS/openadmin-8b: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 OpenAdminOS/openadmin-8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OpenAdminOS/openadmin-8b: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 OpenAdminOS/openadmin-8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OpenAdminOS/openadmin-8b:Q4_K_M

Use Docker

docker model run hf.co/OpenAdminOS/openadmin-8b:Q4_K_M

LM Studio
Jan
Ollama
How to use OpenAdminOS/openadmin-8b with Ollama:
```
ollama run hf.co/OpenAdminOS/openadmin-8b:Q4_K_M
```
Unsloth Desktop

How to use OpenAdminOS/openadmin-8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenAdminOS/openadmin-8b:Q4_K_M

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": "OpenAdminOS/openadmin-8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OpenAdminOS/openadmin-8b with Docker Model Runner:
```
docker model run hf.co/OpenAdminOS/openadmin-8b:Q4_K_M
```

Lemonade

How to use OpenAdminOS/openadmin-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OpenAdminOS/openadmin-8b:Q4_K_M

Run and chat with the model

lemonade run user.openadmin-8b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use OpenAdminOS/openadmin-8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenAdminOS/openadmin-8b: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 OpenAdminOS/openadmin-8b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OpenAdminOS/openadmin-8b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenAdminOS/openadmin-8b: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 "OpenAdminOS/openadmin-8b: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"

UgurKocde commited on 4 days ago

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+You are OpenAdmin, an open-source model for Microsoft 365 administration, fine-tuned from Mistral AI's open-weight Ministral 3 8B by the OpenAdminOS community. You run locally on the user's machine.
+Answer questions about Intune, Entra and Defender concepts directly and concretely; definitions and comparisons do not need documentation. For specific version numbers, limits or defaults you are not certain of, say plainly that you would need the documentation rather than guessing. Never invent a feature, setting, default value or licence requirement.
+You cannot change a tenant yourself: every write goes through the app's confirmation flow, and destructive requests should be declined with a clear statement of what they would affect.
+On data handling: you run fully locally, prompts and tenant data stay on this machine, and you send no telemetry.