Hugging Face fine-tuning in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is fine-tuning in the context of Hugging Face models?

Fine-tuning means taking a pre-trained model and training it a little more on a specific task or dataset to make it work better for that task.

beginner

Why do we use pre-trained models from Hugging Face instead of training from scratch?

Pre-trained models already learned general patterns from large data, so fine-tuning them saves time, needs less data, and usually gives better results than training from scratch.

beginner

What is the role of a tokenizer in Hugging Face fine-tuning?

A tokenizer breaks text into smaller pieces (tokens) that the model understands. It must match the pre-trained model’s tokenizer for fine-tuning to work well.

beginner

What metric is commonly used to check performance when fine-tuning a text classification model?

Accuracy is often used to see how many texts the model correctly classifies after fine-tuning.

intermediate

What is the purpose of the Trainer class in Hugging Face?

Trainer helps manage the training process, like running the training loop, evaluating the model, and saving checkpoints, so you don’t have to write all that code yourself.

What do you need to do before fine-tuning a Hugging Face model on your own text data?

A Load the pre-trained model and tokenizer

B Train a model from scratch

C Skip tokenization

D Use random weights

You must load the pre-trained model and its matching tokenizer before fine-tuning.

Which Hugging Face class helps automate training and evaluation?

A Tokenizer

B Trainer

C Dataset

D Pipeline

Trainer manages training loops, evaluation, and saving models.

Why is fine-tuning faster than training a model from scratch?

A Because the model already learned general features

B Because it uses less data

C Because it skips tokenization

D Because it uses a smaller model

Fine-tuning starts from a model that already learned general patterns, so it needs less time to adapt.

What does the tokenizer do in the fine-tuning process?

A Evaluates the model

B Trains the model

C Saves the model

D Converts text into tokens the model understands

The tokenizer breaks text into tokens for the model to process.

Which metric is commonly used to measure fine-tuning success on classification tasks?

A Training time

B Loss only

C Accuracy

D Number of tokens

Accuracy shows how many examples the model classifies correctly.

Explain the main steps to fine-tune a Hugging Face model on a new text classification task.

Think about loading, preparing data, training, checking results, and saving.

Describe why fine-tuning a pre-trained model is usually better than training a model from scratch.

Consider the benefits of starting with a model that already knows something.