Fine-tune a pretrained model on your own data

What you’ll learn

This is the lesson where you stop using models as-is and start changing them. You will fine-tune a pretrained model on a labeled dataset of your own using the Trainer, then measure whether it actually got better. The source curriculum is the Hugging Face LLM Course, Chapter 3, freely available and Apache-2.0 licensed at huggingface.co/learn/llm-course/chapter3.

You will prepare a dataset and meet the data collator (dynamic padding per batch); understand why loading a model with a task head triggers an expected head-swap warning; set hyperparameters in a single TrainingArguments object; assemble a Trainer and launch training with trainer.train(); and learn the evaluation discipline of a compute_metrics function, turning logits into predictions and scoring them on held-out data with the evaluate library.

Where this fits

This is lesson 3 of 12, the hands-on heart of Phase 1 (the Transformers library). Lesson 2 ran models as-is; this lesson changes them. It works entirely at the lower level lesson 2 opened up (tokenizer, model, logits), which is why opening that box mattered. The next lesson shares the model you fine-tune here, closing the run-adapt-share arc of Phase 1.

Before you start

Prerequisites: lesson 2 of this track (pipeline(), the Auto classes, tokenizers, and logits), all of which this lesson builds on directly. You should be comfortable running Python in a notebook. A GPU is strongly recommended: training on a CPU works but is very slow, and Google Colab’s free GPU tier is the easiest path. Install with pip install transformers datasets evaluate.

About the math

None, but real training happens. This is the most hands-on lesson so far: you will run an actual fine-tuning loop and read real metrics. No formulas; the only near-math is reading logits and taking an argmax, both one line of code. Concepts like learning rate and mixed precision are named and motivated, not derived.

By the end, you’ll be able to

The single capability this lesson builds: fine-tune a pretrained model on a task-specific dataset using the Trainer, and measure the result on held-out data. Concretely, you will be able to:

Prepare a labeled dataset for training with a tokenizer and a DataCollatorWithPadding
Explain why loading a model with a task head triggers an expected head-swap warning
Configure a run with TrainingArguments and assemble a Trainer
Launch fine-tuning with trainer.train()
Evaluate on held-out data with a compute_metrics function (argmax the logits, score with evaluate)

Time and difficulty

Read time: about 12 minutes
Practice time: about 15 minutes (a full fine-tuning run on MRPC, including a few minutes of GPU training, plus flashcards)
Difficulty: standard (the most code-heavy lesson so far, but the Trainer keeps each step short)