Pre-training and fine-tuning concept in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the role of pre-training

Which statement best describes the purpose of pre-training in machine learning models?

A Pre-training initializes a model by learning general patterns from a large dataset before adapting to a specific task.

B Pre-training involves manually labeling data to improve model performance.

C Pre-training is the final step where the model is tested on unseen data to check accuracy.

D Pre-training is used to reduce the size of the dataset by removing irrelevant samples.

💡 Hint

Think about how a model learns general knowledge before focusing on a specific problem.

Pre-training helps the model learn broad features from a large dataset, which can then be fine-tuned for a specific task.

❓ Model Choice

intermediate

Choosing a model for fine-tuning

You want to build a sentiment analysis tool using a large language model. Which model type is best suited for fine-tuning on your specific dataset?

A A simple linear regression model trained from scratch

B A pre-trained transformer model like BERT or GPT

C A random forest model without any pre-training

D A clustering algorithm like K-means

💡 Hint

Consider models that have already learned language patterns and can be adapted.

Pre-trained transformer models have learned language features and can be fine-tuned efficiently for tasks like sentiment analysis.

❓ Predict Output

advanced

Output of fine-tuning training metrics

What will be the output of the training accuracy after fine-tuning this simple model for 3 epochs?

Prompt Engineering / GenAI

import torch
import torch.nn as nn
import torch.optim as optim

class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(2, 2)

    def forward(self, x):
        return self.linear(x)

model = SimpleModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Dummy data: inputs and labels
inputs = torch.tensor([[1.0, 2.0], [2.0, 1.0], [1.5, 1.5], [3.0, 3.0]])
labels = torch.tensor([0, 1, 0, 1])

for epoch in range(3):
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

_, predicted = torch.max(outputs, 1)
correct = (predicted == labels).sum().item()
accuracy = correct / labels.size(0)
print(f"Accuracy after 3 epochs: {accuracy:.2f}")

A Accuracy after 3 epochs: 0.50

B Accuracy after 3 epochs: 1.00

C Accuracy after 3 epochs: 0.25

D Accuracy after 3 epochs: 0.75

💡 Hint

Check the model output and compare predicted labels to true labels after training.

The simple linear model trained for 3 epochs on this small dataset achieves 50% accuracy due to limited training and model capacity.

❓ Hyperparameter

advanced

Effect of learning rate during fine-tuning

During fine-tuning a pre-trained model, what is the typical effect of using a learning rate that is too high?

A The model will ignore the pre-trained weights and start training from scratch.

B The model will always converge faster and achieve better accuracy.

C The model will reduce overfitting by stopping training early.

D The model may fail to converge and training loss can fluctuate or increase.

💡 Hint

Think about how big steps in learning affect the model's ability to settle on good solutions.

A too high learning rate can cause the model to jump around the solution space, preventing stable convergence.

🔧 Debug

expert

Identifying the cause of poor fine-tuning results

You fine-tuned a large pre-trained model on a small dataset but the validation accuracy is very low and training loss does not improve. Which issue is most likely causing this?

A The model architecture is too simple for the task.

B The dataset is too large, causing overfitting.

C The learning rate is too high, causing unstable training.

D The pre-training was done on a similar domain.

💡 Hint

Consider what happens when training a complex model on limited data with aggressive settings.

A high learning rate can prevent the model from properly adjusting weights during fine-tuning, leading to poor accuracy and no loss improvement.

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