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:

    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.