This repository is the home for a weekly, hands-on AI workshop. Sessions are designed for 1 hour each and assume a mix of experience levels.
The core lessons are designed to run on CPU. If a participant has a supported GPU or Apple Silicon acceleration, the setup checks will report it, but GPU access is not required until the advanced vLLM session.
| Session | Topic | Outcome |
|---|---|---|
| 01 | Setup | Install Python tooling, create a virtual environment, install packages, and run a script and notebook in VS Code. |
| 02 | Tensors and PyTorch | Create tensors, inspect shapes, and run basic PyTorch operations. |
| 03 | MLP MNIST Training Loop | Train a small neural network on MNIST using a local PyTorch training loop. |
| 04 | Training Loop Improvements | Add validation, metrics, checkpoints, and cleaner training structure. |
| 05 | Hugging Face Basics | Use tokenizers, datasets, pretrained models, and the model hub. |
| 06 | Generative Models | Run small pretrained generative models locally. |
| 07 | Fine-Tuning Generative Models | Adapt a small generative model with a focused training loop. |
| 08 | Advanced vLLM Instruction Tuning | Explore instruction-tuning and vLLM workflows for suitable GPU or cloud environments. |
- CPU is enough for the core workshop path.
- Apple Silicon Macs may use MPS acceleration if PyTorch supports the machine.
- Windows/Linux machines may use CUDA if the right NVIDIA drivers and PyTorch build are installed.
- Advanced LLM training and vLLM may require cloud GPU access.
Each lesson is a top-level directory. Lessons may include:
README.mdfor the lesson plan and instructions- Python scripts for repeatable runs
- notebooks for exploration
exercises/for small incomplete coding taskssolutions/for completed versions of those tasks
The shared environment for the core sessions is defined in requirements.txt. Advanced dependencies are separated in requirements-advanced.txt.