2027 Decision AI LLM Fine Tuning: Make Your Own System 1 LLM

About this course

This course contains the use of artificial intelligence. Learn to fine-tune transformer models in Python, step by step, until you have trained a decision model of your own. In this hands-on course you use PyTorch, Hugging Face Transformers, the Trainer, datasets, evaluate, PEFT and bitsandbytes to fine-tune BERT-style encoders and small LLMs for text classification, check how far you can trust their confidence, and train with LoRA and QLoRA.In Jev AI and Laya Fundamentals you called a decision model through an API. It read a support ticket or a bank message and answered a typed question, with a probability for every option. This course opens that kind of model up and shows you how to build, fine-tune and calibrate one yourself, on real data, with code that runs on a free GPU in Google Colab or Kaggle.

What makes this course different

You see the machine before the code. Every idea (tokens, attention, the causal mask, logits, softmax, the next-token loss, a classification head, LoRA) is first drawn as an animated scene on the real data, then typed in a notebook line by line, with the real output read out.

Text classification with three kinds of model. On 40 support tickets you compare a ready-made sentiment model, zero-shot classification and a chat model, then you learn why an encoder like ModernBERT is the model that decides.

A transformer opened step by step in PyTorch: text becomes tokens and vectors, every token attends to every other token, the scores become weights, and you watch a real Qwen model predict the next token and compute its loss.

Confidence you can check. You read the answer from the option letters in one forward pass instead of asking for JSON, measure expected calibration error, and fix over-confidence with temperature scaling.

Fine-tuning Laya on custom data.

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