--- title: "How To Train Your LLM 🐉" slug: "how-to-train-your-llm" canonical_url: "https://ai-weeks.ch/events/how-to-train-your-llm" start_date: "2026-10-02T12:00:00.000Z" end_date: "2026-10-02T16:00:00.000Z" attendance_mode: "offline" language: "en" price_type: "free" price: "Free" location: "Zurich TBC" organizer_url: "https://www.linkedin.com/in/sonyablibli/" event_url: "https://luma.com/lezwxgzj" image: "https://ai-weeks.ch/api/airtable/events/recHAKOCGidvhxz1c/event_image_16x9_web?year=195" tags: [] status: "published" description: "### **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…" --- # 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. ## Details - **Start:** 2026\-10\-02T12:00:00\.000Z - **End:** 2026\-10\-02T16:00:00\.000Z - **Location:** Zurich TBC - **Attendance:** offline - **Language:** en - **Price:** Free - **Audience:** developers, builders, students, vibe coders, curious people - **Event page:** Canonical: