Hugging Face fine-tuning in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Hugging Face fine-tuning

This pipeline shows how a pre-trained language model from Hugging Face is fine-tuned on a new text dataset to improve its performance on a specific task, like sentiment analysis or question answering.

Data Flow - 5 Stages

1Data Loading

1000 rows x 2 columns→Load raw text data and labels from CSV→1000 rows x 2 columns

Row: {text: 'I love this movie!', label: 'positive'}

↓

2Preprocessing

1000 rows x 2 columns→Tokenize text into input IDs and attention masks→1000 rows x 128 tokens

Input IDs: [101, 1045, 2293, 2023, 3185, 999, 102]

↓

3Train/Test Split

1000 rows x 128 tokens→Split data into 800 training and 200 testing samples→Train: 800 rows x 128 tokens, Test: 200 rows x 128 tokens

Train sample input IDs: [101, 1045, 2293, 2023, 3185, 999, 102]

↓

4Model Fine-tuning

800 rows x 128 tokens→Train pre-trained model on training data for 3 epochs→Fine-tuned model weights

Model updates weights to better predict sentiment

↓

5Evaluation

200 rows x 128 tokens→Predict labels on test data and compute accuracy→Accuracy score (e.g., 0.88)

Predicted labels vs true labels compared

Training Trace - Epoch by Epoch

Loss
0.7 |*       
0.6 |**      
0.5 |***     
0.4 |****    
0.3 |*****   
0.2 |        
    +--------
     1 2 3 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.70	Model starts learning, loss decreases from initial high value
2	0.42	0.82	Loss decreases further, accuracy improves significantly
3	0.30	0.88	Model converges with low loss and high accuracy

Prediction Trace - 5 Layers

Layer 1: Tokenization

Layer 2: Embedding Layer

Layer 3: Transformer Layers

Layer 4: Classification Head

Layer 5: Prediction

Model Quiz - 3 Questions

What happens to the loss value during fine-tuning?

A It stays the same

B It increases steadily over epochs

C It decreases steadily over epochs

D It randomly jumps up and down

Key Insight

Fine-tuning a pre-trained Hugging Face model adapts it to a new task by updating weights with new data, improving accuracy while starting from a strong base. The loss decreases and accuracy increases as the model learns.

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