Why fine-tuning adapts models to domains in Prompt Engineering / GenAI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained model for fine-tuning.

Prompt Engineering / GenAI

model = load_model('[1]')

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

Apretrained-domain-model

Bempty-model

Crandom-model

Dbase-model

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a model without prior training like 'random-model'.

We start fine-tuning from a base-model that already learned general knowledge.

2fill in blank

medium

Complete the code to prepare domain-specific data for fine-tuning.

Prompt Engineering / GenAI

domain_data = load_data('[1]')

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

Arandom_text.txt

Bgeneral_corpus.txt

Cdomain_corpus.txt

Dempty_file.txt

Attempts:

3 left

💡 Hint

Common Mistakes

Using general or unrelated data files for fine-tuning.

Fine-tuning uses domain_corpus.txt to adapt the model to the specific domain.

3fill in blank

hard

Fix the error in the fine-tuning loop by completing the missing method call.

Prompt Engineering / GenAI

for batch in domain_data:
    loss = model.[1](batch)
    loss.backward()

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

Aforward

Btrain

Cpredict

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' or 'evaluate' which are for inference, not training.

The forward method computes the output and loss during training.

4fill in blank

hard

Fill both blanks to update the model parameters during fine-tuning.

Prompt Engineering / GenAI

optimizer.[1]()
optimizer.[2]()

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

Azero_grad

Bstep

Cbackward

Dforward

Attempts:

3 left

💡 Hint

Common Mistakes

Calling 'backward' or 'forward' on optimizer which is incorrect.

First, zero_grad() clears old gradients. Then, step() updates model weights.

5fill in blank

hard

Fill all three blanks to create a dictionary of fine-tuned model metrics.

Prompt Engineering / GenAI

metrics = {'loss': [1], 'accuracy': [2], 'epoch': [3]

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

Aloss_value

Bacc_value

Ccurrent_epoch

Dmodel_output

Attempts:

3 left

💡 Hint

Common Mistakes

Using model output instead of metric values.

We store the current loss_value, acc_value, and current_epoch in the metrics dictionary.

Practice

1. Why do we fine-tune a pre-trained model for a specific domain?

easy

A. To make the model larger and more complex

B. To reduce the model's accuracy on general tasks

C. To erase all previous knowledge from the model

D. To help the model learn details specific to that domain

Solution

1. Step 1: Understand the purpose of fine-tuning

   Fine-tuning adjusts a general model to perform better on a specific topic or style by teaching it new details.

2. Step 2: Identify the effect on the model

   Fine-tuning helps the model learn domain-specific details without losing all previous knowledge.

3. Final Answer:

   To help the model learn details specific to that domain -> Option D

4. Quick Check:

   Fine-tuning = domain adaptation [OK]

Hint: Fine-tuning adds domain details, not erases knowledge [OK]

Common Mistakes:

• Thinking fine-tuning makes the model forget everything
• Believing fine-tuning always makes the model bigger
• Assuming fine-tuning reduces accuracy on all tasks

2. Which of the following is the correct way to start fine-tuning a model in Python using a library?

easy

A. model.fine_tune(data, epochs=3)

B. model.train(data, epochs=3)

C. model.fit(data, epochs=3)

D. model.tune(data, epochs=3)

Solution

1. Step 1: Recognize common fine-tuning method names

   In many ML libraries, fit is used to train or fine-tune models on new data.

2. Step 2: Compare options to common usage

   fine_tune and tune are not standard method names; train is less common than fit for fine-tuning.

3. Final Answer:

   model.fit(data, epochs=3) -> Option C

4. Quick Check:

   Fine-tuning uses fit() method [OK]

Hint: Use fit() to train or fine-tune models in Python [OK]

Common Mistakes:

• Choosing non-existent method names like fine_tune()
• Confusing train() with fit() in common libraries
• Assuming tune() is a valid method

3. Given this code snippet for fine-tuning a model, what will be the output loss after training?

initial_loss = 0.8
for epoch in range(3):
    initial_loss *= 0.7
print(round(initial_loss, 2))

medium

A. 0.27

B. 0.41

C. 0.56

D. 0.34

Solution

1. Step 1: Calculate loss after each epoch

   Start with 0.8, multiply by 0.7 three times: 0.8 * 0.7 = 0.56, 0.56 * 0.7 = 0.392, 0.392 * 0.7 = 0.2744.

2. Step 2: Round the final loss

   Rounded to two decimals: 0.27.

3. Final Answer:

   0.27 -> Option A

4. Quick Check:

   Loss after 3 epochs = 0.27 [OK]

Hint: Multiply loss by decay each epoch, then round [OK]

Common Mistakes:

• Multiplying fewer times than epochs
• Rounding before final multiplication
• Choosing wrong rounded value

4. You tried fine-tuning a model but the accuracy did not improve. Which of these is the most likely error in your code?

model = load_pretrained_model()
model.fit(new_data)
model.evaluate(test_data)

medium

A. Not specifying epochs in fit() so training was too short

B. Using evaluate() before fit()

C. Loading the wrong model type

D. Not normalizing the test data

Solution

1. Step 1: Check the fit() method usage

   Without specifying epochs, fit() may run only one epoch or default minimal training, insufficient for fine-tuning.

2. Step 2: Understand impact on accuracy

   Too few training steps means the model doesn't learn new domain details, so accuracy stays low.

3. Final Answer:

   Not specifying epochs in fit() so training was too short -> Option A

4. Quick Check:

   Short training = no accuracy gain [OK]

Hint: Always set epochs to train enough during fine-tuning [OK]

Common Mistakes:

• Assuming evaluate() order matters before fit()
• Ignoring data normalization effects
• Not checking model type mismatch

5. You have a general language model and want it to perform well on medical text. Which fine-tuning approach best adapts it to this domain?

hard

A. Train the model from scratch only on medical data

B. Fine-tune the pre-trained model with a small medical dataset using low learning rate

C. Use the pre-trained model without any changes

D. Fine-tune the model with random unrelated data to increase size

Solution

1. Step 1: Compare training from scratch vs fine-tuning

   Training from scratch needs lots of data and time; fine-tuning uses existing knowledge and adapts efficiently.

2. Step 2: Identify best fine-tuning practice

   Using a small medical dataset with a low learning rate helps the model learn domain details without forgetting general knowledge.

3. Final Answer:

   Fine-tune the pre-trained model with a small medical dataset using low learning rate -> Option B

4. Quick Check:

   Fine-tune + small data + low rate = best domain fit [OK]

Hint: Fine-tune with small domain data and low learning rate [OK]

Common Mistakes:

• Training from scratch without enough data
• Using unrelated data for fine-tuning
• Skipping fine-tuning and using general model only