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.

Practice

1. What is the main purpose of fine-tuning a pre-trained model using Hugging Face?

easy

A. To adapt the model to perform well on a specific new task

B. To train a model from scratch without any prior knowledge

C. To reduce the size of the model for faster inference

D. To convert the model into a different programming language

Solution

1. Step 1: Understand what fine-tuning means

   Fine-tuning means taking a model already trained on a large dataset and adjusting it to work well on a new, specific task.

2. Step 2: Identify the purpose in Hugging Face context

   Hugging Face fine-tuning adapts the pre-trained model's knowledge to your task, improving accuracy without training from scratch.

3. Final Answer:

   To adapt the model to perform well on a specific new task -> Option A

4. Quick Check:

   Fine-tuning = adapt model to new task [OK]

Hint: Fine-tuning means adjusting a model for your task [OK]

Common Mistakes:

• Thinking fine-tuning trains a model from scratch
• Confusing fine-tuning with model compression
• Assuming fine-tuning changes the programming language

2. Which of the following is the correct way to create a TrainingArguments object in Hugging Face?

easy

A. training_args = TrainArgs(directory='output', epochs=3)

B. training_args = TrainerArguments(output='output', epochs=3)

C. training_args = Training(output_dir='output', epochs=3)

D. training_args = TrainingArguments(output_dir='output', num_train_epochs=3)

Solution

1. Step 1: Recall the correct class name and parameters

   The Hugging Face library uses the class TrainingArguments with parameters like output_dir and num_train_epochs.

2. Step 2: Match the correct syntax

   training_args = TrainingArguments(output_dir='output', num_train_epochs=3) uses the correct class name and parameter names exactly as in the Hugging Face API.

3. Final Answer:

   training_args = TrainingArguments(output_dir='output', num_train_epochs=3) -> Option D

4. Quick Check:

   TrainingArguments with output_dir and num_train_epochs [OK]

Hint: Use TrainingArguments with output_dir and num_train_epochs [OK]

Common Mistakes:

• Using wrong class names like TrainerArguments or TrainArgs
• Using incorrect parameter names like epochs instead of num_train_epochs
• Confusing Trainer and TrainingArguments classes

3. Given the code snippet below, what will be the output of print(len(tokenized_datasets['train'][0]['input_ids']))?

from datasets import load_dataset
from transformers import AutoTokenizer

dataset = load_dataset('imdb', split='train[:1%]')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
tokenized_datasets = dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding='max_length', max_length=128))

medium

A. None, it will raise an error

B. 128

C. 512

D. variable length depending on text

Solution

1. Step 1: Understand tokenizer parameters

   The tokenizer is called with padding='max_length' and max_length=128, so all sequences are padded or truncated to length 128.

2. Step 2: Check the length of input_ids

   Since padding to max_length is applied, each tokenized input's input_ids list length is exactly 128.

3. Final Answer:

   128 -> Option B

4. Quick Check:

   Padding to max_length = fixed length 128 [OK]

Hint: Padding with max_length fixes token length [OK]

Common Mistakes:

• Assuming variable length without padding
• Confusing max_length with 512 default
• Expecting error due to missing batch=True

4. You wrote this code to fine-tune a model but get an error: TypeError: Trainer() missing 1 required positional argument: 'model'. What is the likely fix?

medium

A. Change Trainer to TrainingArguments

B. Remove the 'model' argument from Trainer initialization

C. Pass the pre-trained model as the 'model' argument when creating Trainer

D. Call Trainer.train() before creating the Trainer object

Solution

1. Step 1: Understand the error message

   The error says the Trainer constructor needs a 'model' argument but it was not provided.

2. Step 2: Fix by providing the model

   When creating a Trainer, you must pass the pre-trained model as the 'model' parameter to avoid this error.

3. Final Answer:

   Pass the pre-trained model as the 'model' argument when creating Trainer -> Option C

4. Quick Check:

   Trainer requires model argument [OK]

Hint: Always pass model to Trainer constructor [OK]

Common Mistakes:

• Forgetting to pass model to Trainer
• Confusing Trainer with TrainingArguments
• Calling train() before creating Trainer

5. You want to fine-tune a Hugging Face model on a small dataset but avoid overfitting. Which combination of TrainingArguments settings is best?

hard

A. Set num_train_epochs=3 and use evaluation_strategy='steps' with early stopping

B. Set num_train_epochs=10 and learning_rate=5e-5

C. Set batch_size=1 and disable evaluation

D. Set num_train_epochs=1 and learning_rate=1.0

Solution

1. Step 1: Identify overfitting prevention methods

   Using fewer epochs and evaluation with early stopping helps stop training before overfitting.

2. Step 2: Evaluate options for best practice

   Set num_train_epochs=3 and use evaluation_strategy='steps' with early stopping sets a moderate number of epochs and enables evaluation with early stopping, which is best to avoid overfitting.

3. Final Answer:

   Set num_train_epochs=3 and use evaluation_strategy='steps' with early stopping -> Option A

4. Quick Check:

   Early stopping + moderate epochs prevent overfitting [OK]

Hint: Use early stopping and moderate epochs to avoid overfitting [OK]

Common Mistakes:

• Using too many epochs causing overfitting
• Setting learning rate too high or too low
• Ignoring evaluation and early stopping