How To Train Your LLM 🐉

A hands-on beginner workshop on what actually happens inside an LLM.

You’ve used ChatGPT. Maybe you’ve played with prompts, APIs or open models.
But what does it actually mean to train a language model?
In this 4-hour workshop, we’ll go from a tiny model that barely knows how to speak to a real pretrained LLM that we can fine-tune ourselves.
No AI magic. No 80-slide lecture. We’ll actually build things.

What we’ll do

• Turn text into tokens and see how next-token prediction works
• Train a tiny language model from scratch
• See how a Transformer improves on our first model
• Experiment with temperature, training time and model size
• Run a real open-source LLM on a GPU
• Understand CPU vs. GPU, memory and quantization
• Fine-tune a pretrained LLM with LoRA
• Compare the model before and after training

You’ll leave understanding

Training vs. inference. Pretraining vs. fine-tuning. Tokens, parameters, Transformers, GPUs, LoRA and why any of it matters.
More importantly, you’ll have actually used these concepts yourself instead of just hearing about them.

Who is this for?

Developers, students, data/product people and technically curious humans who want to understand LLMs beyond prompting.
You don’t need previous machine-learning experience.
Basic Python familiarity will help, but the exercises are guided.

Format

4 hours · Beginner-friendly · Hands-on
We’ll use a prepared cloud environment with access to GPUs, so you won’t need to install CUDA, own a powerful laptop or spend half the workshop debugging dependencies.
Bring your laptop and curiosity.