LLM Finetuning
This track covers the practical skills for adapting pretrained large language models to your own data. You'll learn data preparation, parameter-efficient methods like LoRA and QLoRA, training loops with Hugging Face Transformers, and evaluation strategies. Ideal for ML engineers and developers wanting to customize LLMs for specific tasks, it ends with deploying a fine-tuned model responsibly.
Lesson outline
-
49
Resume Training from Checkpoints
Resume training from a checkpoint safely — LLM Finetuning tutorial, lesson 50. Learn key steps, troubleshooting, and what to study next.
-
50
Deploy a Fine-Tuned LLM Safely
Deploy a fine-tuned LLM to production safely. Learn key practices for secure and reliable deployment of your custom model.
-
51
Monitor Model Drift
Monitor model drift after continuous finetuning — LLM Finetuning tutorial, lesson 52. Learn hands-on steps, troubleshooting, and what to study next.
-
52
Build a Responsible AI Checklist
Build a responsible AI checklist for LLM release — essential steps to ensure ethical, safe, and compliant deployment of your fine-tuned model.
LLM Finetuning — step-by-step tutorials
What you will find here
This track walks through llm finetuning in order — each lesson is server-rendered HTML you can read without JavaScript. Follow the outline, then practice in the browser IDE when a lesson links to runnable code.
Tutorials vs quizzes and code samples
Tutorials teach in sequence. For quick checks use quizzes. For copy-paste snippets see code samples. For deeper reading browse articles.