Pre-training and fine-tuning concept in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained model.

Prompt Engineering / GenAI

model = load_model('[1]')

Drag options to blanks, or click blank then click option'

Arandom_weights

Bpretrained_weights

Cuntrained_model

Dempty_model

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing random or empty weights which means no prior learning.

Loading pretrained_weights means the model already learned from a large dataset.

2fill in blank

medium

Complete the code to fine-tune the model on new data.

Prompt Engineering / GenAI

model.train(new_data, epochs=[1])

Drag options to blanks, or click blank then click option'

A1000

B0

C-1

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using zero epochs means no training.

Using very large epochs may overfit the model.

Fine-tuning usually involves training for a few epochs like 5 to adjust the model gently.

3fill in blank

hard

Fix the error in the code to freeze pre-trained layers before fine-tuning.

Prompt Engineering / GenAI

for layer in model.layers:
    layer.[1] = False

Drag options to blanks, or click blank then click option'

Atrainable

Bfrozen

Clocked

Dfixed

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent properties like 'frozen' or 'locked'.

Setting trainable = False freezes the layer so it is not updated during fine-tuning.

4fill in blank

hard

Fill both blanks to create a fine-tuning loop that updates only unfrozen layers.

Prompt Engineering / GenAI

for layer in model.layers:
    if layer.[1] == [2]:
        update(layer)

Drag options to blanks, or click blank then click option'

Atrainable

BTrue

CFalse

Dfrozen

Attempts:

3 left

💡 Hint

Common Mistakes

Checking for 'frozen' property which does not exist.

Using False instead of True to update layers.

We check if trainable == True to update only layers that can learn.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that stores layer names and their trainable status.

Prompt Engineering / GenAI

layer_status = {layer.[1]: layer.[2] for layer in model.[3]

Drag options to blanks, or click blank then click option'

Aname

Btrainable

Clayers

Dweights

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'weights' instead of 'layers' to iterate.

Mixing up 'name' and 'trainable' properties.

This code creates a dictionary with layer names as keys and their trainable status as values from the model's layers.

Practice

1. What is the main purpose of pre-training in machine learning models?

easy

A. To delete unnecessary data from the model

B. To adjust the model for a specific task

C. To evaluate the model's performance

D. To teach the model general knowledge from large data

Solution

1. Step 1: Understand pre-training role

   Pre-training is done on large datasets to help the model learn general patterns and knowledge.

2. Step 2: Differentiate from fine-tuning

   Fine-tuning is the step where the model is adapted to a specific task, not the initial general learning.

3. Final Answer:

   To teach the model general knowledge from large data -> Option D

4. Quick Check:

   Pre-training = General knowledge learning [OK]

Hint: Pre-training = general learning, fine-tuning = task-specific [OK]

Common Mistakes:

• Confusing pre-training with fine-tuning
• Thinking pre-training is for evaluation
• Assuming pre-training deletes data

2. Which of the following is the correct way to describe fine-tuning?

easy

A. Adjusting a pre-trained model to perform a specific task

B. Removing layers from a neural network

C. Training a model from scratch on a small dataset

D. Collecting data for training

Solution

1. Step 1: Define fine-tuning

   Fine-tuning means taking a model already trained on general data and adjusting it for a specific task.

2. Step 2: Eliminate incorrect options

   Training from scratch is not fine-tuning; removing layers or collecting data are unrelated to fine-tuning.

3. Final Answer:

   Adjusting a pre-trained model to perform a specific task -> Option A

4. Quick Check:

   Fine-tuning = adapt pre-trained model [OK]

Hint: Fine-tuning = adapt model, not train from scratch [OK]

Common Mistakes:

• Confusing fine-tuning with training from scratch
• Thinking fine-tuning means changing model structure
• Mixing data collection with fine-tuning

3. Consider this Python-like pseudocode for fine-tuning a pre-trained model:

model = load_pretrained_model()
model.train(specific_task_data)
predictions = model.predict(test_data)
print(predictions)

What is the expected output of print(predictions)?

medium

A. Random values unrelated to the task

B. Predictions based on the specific task after fine-tuning

C. Error because model is not trained

D. Predictions from the original pre-trained model without changes

Solution

1. Step 1: Understand the code flow

   The model is loaded pre-trained, then trained on specific task data (fine-tuning), then used to predict.

2. Step 2: Predict output meaning

   After fine-tuning, predictions reflect the model adapted to the specific task, not random or original outputs.

3. Final Answer:

   Predictions based on the specific task after fine-tuning -> Option B

4. Quick Check:

   Fine-tuned model predicts task data [OK]

Hint: Fine-tuned model predicts task-specific outputs [OK]

Common Mistakes:

• Assuming predictions are random
• Thinking model is untrained
• Ignoring fine-tuning effect on predictions

4. You try to fine-tune a pre-trained model but get an error: AttributeError: 'NoneType' object has no attribute 'train'. What is the most likely cause?

medium

A. The pre-trained model failed to load, returning None

B. The training data is empty

C. The model is already fine-tuned

D. The prediction method is called before training

Solution

1. Step 1: Analyze the error message

   The error says 'NoneType' has no attribute 'train', meaning the model variable is None, not a model object.

2. Step 2: Identify cause of None

   This usually happens if loading the pre-trained model failed and returned None instead of a model.

3. Final Answer:

   The pre-trained model failed to load, returning None -> Option A

4. Quick Check:

   None model means load failure [OK]

Hint: Check if model loaded correctly before training [OK]

Common Mistakes:

• Blaming empty training data for this error
• Assuming model is already fine-tuned
• Confusing training and prediction order

5. You have a large language model pre-trained on general text. You want to create a chatbot for medical advice. Which approach best uses pre-training and fine-tuning?

hard

A. Use the pre-trained model without any changes

B. Train a new model from scratch only on medical texts

C. Fine-tune the pre-trained model on medical conversation data

D. Pre-train the model again on medical texts before fine-tuning

Solution

1. Step 1: Understand the goal

   The goal is to adapt a general language model to a specific medical chatbot task.

2. Step 2: Evaluate options

   Training from scratch is costly and slow; using the model without changes lacks medical knowledge; re-pre-training is unnecessary and expensive.

3. Step 3: Choose best approach

   Fine-tuning the pre-trained model on medical conversation data efficiently adapts it to the task.

4. Final Answer:

   Fine-tune the pre-trained model on medical conversation data -> Option C

5. Quick Check:

   Fine-tuning adapts general model to specific task [OK]

Hint: Fine-tune pre-trained model for specific tasks [OK]

Common Mistakes:

• Training from scratch wastes resources
• Using pre-trained model without fine-tuning misses task needs
• Re-pre-training is redundant and costly