diff --git a/README.md b/README.md
index cfa5f99..acd0a07 100644
--- a/README.md
+++ b/README.md
@@ -32,14 +32,17 @@ Ollama (port 11434 on the GPU server) is used for model management — pulling,
 
 ### Prerequisites
 
-- Docker + Docker Compose
 - A remote machine with:
   - SSH access
   - `miniconda3` with a `synthetic-data` conda env containing `synthetic-data-kit`
   - `train.py` at `/opt/synthetic/train.py`
   - Ollama running on port `11434`
 
-### Run
+---
+
+### Option A — Docker (quickest)
+
+**Additional requirements:** Docker + Docker Compose
 
 ```bash
 docker compose up --build
@@ -53,6 +56,96 @@ docker compose up --build
 
 The `OLLAMA_URL` environment variable in `docker-compose.yml` defaults to `http://192.168.2.47:11434` — update it to point to your GPU server.
 
+---
+
+### Option B — Install as a system package (Debian / Ubuntu)
+
+The package is published to the local Gitea registry and installs the FastAPI backend as a systemd service with nginx serving the frontend on port **3000**.
+
+**1. Add the apt source**
+
+```bash
+echo "deb [trusted=yes] http://192.168.1.64:3000/api/packages/tocmo0nlord/debian bookworm main" \
+  | sudo tee /etc/apt/sources.list.d/llm-trainer.list
+sudo apt update
+```
+
+**2. Install**
+
+```bash
+sudo apt install llm-trainer
+```
+
+The installer will automatically:
+- Create a `llm-trainer` system user
+- Install the FastAPI backend under `/opt/llm-trainer/` with its own Python venv
+- Enable and start the `llm-trainer` systemd service
+- Configure nginx to serve the React frontend on port **3000** and proxy `/api/` to the backend
+
+| Service | URL |
+|---------|-----|
+| Frontend | `http://<server-ip>:3000` |
+| Backend API | `http://<server-ip>:3000/api` |
+| API docs | `http://<server-ip>:8080/docs` |
+
+**3. Configure**
+
+Edit `/etc/llm-trainer/env` to set the Ollama URL for your GPU server:
+
+```ini
+OLLAMA_URL=http://192.168.2.47:11434
+```
+
+Then restart the service:
+
+```bash
+sudo systemctl restart llm-trainer
+```
+
+**Service management**
+
+```bash
+sudo systemctl status llm-trainer
+sudo systemctl restart llm-trainer
+sudo journalctl -u llm-trainer -f          # live logs
+sudo tail -f /var/log/llm-trainer/backend.log
+```
+
+**Uninstall**
+
+```bash
+sudo apt remove llm-trainer      # keep config and logs
+sudo apt purge llm-trainer       # remove everything including /opt/llm-trainer
+```
+
+---
+
+### Building the .deb from source
+
+To rebuild the package (e.g. after code changes):
+
+```bash
+# Install build dependencies
+sudo apt install -y git nodejs npm python3 python3-pip python3-venv nginx
+
+# Clone and build
+git clone http://192.168.1.64:3000/tocmo0nlord/llm-trainer.git
+cd llm-trainer
+chmod +x packaging/build-deb.sh
+./packaging/build-deb.sh
+# Produces llm-trainer_1.0.0_amd64.deb in the repo root
+
+# Install locally
+sudo dpkg -i llm-trainer_1.0.0_amd64.deb
+sudo apt-get install -f   # resolve any missing runtime deps
+
+# Or upload to the Gitea registry
+curl -u tocmo0nlord:<token> --upload-file llm-trainer_1.0.0_amd64.deb \
+  http://192.168.1.64:3000/api/packages/tocmo0nlord/debian/pool/bookworm/main/upload
+```
+
+---
+
 ### Configuration
 
 The pipeline reads its config from `/opt/synthetic/synthetic-data-kit/config.yaml` on the remote server. You can edit it live from the **Config Editor** tab in the UI.