# Quickstart

## 1. Fork and clone

1. Click **Fork** in the top-right corner of this repo on GitHub to create your own copy.
2. Clone your fork:

   ```bash
   git clone https://github.com/<your-username>/llms-demo.git
   ```

## 2. Open in a dev container

1. Open the cloned folder in VS Code.
2. When prompted **"Reopen in Container"**, click it (or run the command **Dev Containers: Reopen in Container** from the Command Palette `Ctrl+Shift+P`).
3. VS Code will build and start the container. This takes a few minutes the first time.

## 3. What happens during container startup

The dev container is based on the `gperdrizet/deeplearning-gpu` image (NVIDIA GPU-enabled). On first creation, the `postCreateCommand` runs automatically and does the following:

| Step | What it does |
|------|-------------|
| `mkdir -p models/hugging_face && mkdir -p models/ollama` | Creates local directories for model storage |
| `pip install -r requirements.txt` | Installs Python dependencies: **gradio**, **huggingface-hub**, **langchain-ollama**, **openai**, **python-dotenv**, **torch**, **transformers** |
| `bash .devcontainer/install_ollama.sh` | Downloads and installs the Ollama CLI |

The container also pre-configures the following:

| Setting | Detail |
|---------|--------|
| **GPU access** | All host GPUs are passed through (`--gpus all`) |
| **Python interpreter** | `/usr/bin/python` is set as the default |
| **`HF_HOME`** | Points to `models/hugging_face` so Hugging Face downloads stay in the repo |
| **`OLLAMA_MODELS`** | Points to `models/ollama` so Ollama downloads stay in the repo |
| **Port 7860** | Forwarded automatically for Gradio web UIs |
| **VS Code extensions** | Python, Jupyter, Code Spell Checker, and Marp (slide viewer) are installed |

Once the container is ready you can start running the demos - no extra setup needed.