Hugging Face: Fine-tune a Pretrained Model
by Hugging Face
The canonical starting tutorial for fine-tuning any model with the Transformers Trainer.
Overview
This is the reference tutorial almost everyone uses for their first fine-tune. It shows how to load a dataset, tokenize it, and fine-tune a pretrained model using either the high-level Trainer API or a native PyTorch loop, then evaluate and save it. Because it's part of the Transformers docs, it stays current with the library and links out to task-specific guides (classification, summarization, token classification, and more).
At a Glance
- Topic
- Fine-Tuning
- Level
- Intermediate
- Format
- Tutorial
- Cost
- Free
- Duration
- ~1-2 hours
- Provider
- Hugging Face
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Preparing and tokenizing a dataset
- ✓Fine-tuning with the Trainer API
- ✓Writing a native PyTorch training loop
- ✓Evaluating and saving your model
Highlights
- •Official and always current
- •Both high-level and low-level paths
Who It’s For
Best For
- ✓Developers doing their first fine-tune
Prerequisites
- •Python
- •PyTorch basics
FAQ
What is Hugging Face: Fine-tune a Pretrained Model?
The official Transformers tutorial for fine-tuning a pretrained model on your own dataset using the Trainer API or native PyTorch.
Is Hugging Face: Fine-tune a Pretrained Model free?
Hugging Face: Fine-tune a Pretrained Model is free to access.
What level is Hugging Face: Fine-tune a Pretrained Model for?
Hugging Face: Fine-tune a Pretrained Model is aimed at a intermediate audience. Recommended background: Python, PyTorch basics.
How long does Hugging Face: Fine-tune a Pretrained Model take?
Expect roughly ~1-2 hours. Most learners work through it at their own pace.
What will I learn from Hugging Face: Fine-tune a Pretrained Model?
You'll learn: Preparing and tokenizing a dataset; Fine-tuning with the Trainer API; Writing a native PyTorch training loop; Evaluating and saving your model.