Evaluation of fine-tuned models in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Evaluation of fine-tuned models

This pipeline shows how a pre-trained model is fine-tuned on new data and then evaluated to check its performance. Fine-tuning adjusts the model to better fit the new task, and evaluation measures how well it learned.

Data Flow - 5 Stages

1Pre-trained model loading

N/A→Load a general model trained on large data→Model ready for fine-tuning

A language model trained on general text

↓

2Fine-tuning dataset preparation

1000 rows x 10 columns→Select and preprocess task-specific data→1000 rows x 10 columns

Customer reviews labeled positive or negative

↓

3Fine-tuning the model

Model + 1000 rows x 10 columns→Train model weights on new data with small learning rate→Fine-tuned model

Model adjusts to classify customer reviews

↓

4Evaluation dataset preparation

200 rows x 10 columns→Prepare separate test data not seen during training→200 rows x 10 columns

New customer reviews with labels

↓

5Model evaluation

Fine-tuned model + 200 rows x 10 columns→Predict and compare with true labels to compute metrics→Accuracy, Precision, Recall, F1-score

Model predicts 180 correct out of 200 reviews

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |**  
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning task-specific patterns
2	0.48	0.75	Loss decreases, accuracy improves
3	0.35	0.82	Model fine-tunes well on new data
4	0.30	0.85	Training converges with good accuracy
5	0.28	0.87	Slight improvement, model stabilizes

Prediction Trace - 5 Layers

Layer 1: Input processing

Layer 2: Embedding layer

Layer 3: Fine-tuned model layers

Layer 4: Softmax activation

Layer 5: Final prediction

Model Quiz - 3 Questions

What does the decreasing loss during fine-tuning indicate?

A The model is forgetting previous knowledge

B The model is learning to make better predictions

C The data is becoming more complex

D The training stopped early

Key Insight

Fine-tuning adjusts a general model to a specific task, improving accuracy. Evaluating on new data ensures the model truly learned and can generalize well.

Practice

1. What is the main purpose of evaluating a fine-tuned model?

easy

A. To reduce the number of model layers

B. To check how well the model performs on new, unseen data

C. To speed up the training process

D. To increase the size of the training dataset

Solution

1. Step 1: Understand model evaluation

   Evaluation measures how well the model predicts on data it has not seen before.

2. Step 2: Identify the purpose of evaluation

   It helps us know if the model learned useful patterns or just memorized training data.

3. Final Answer:

   To check how well the model performs on new, unseen data -> Option B

4. Quick Check:

   Evaluation = performance on new data [OK]

Hint: Evaluation checks model on new data, not training data [OK]

Common Mistakes:

• Confusing evaluation with training
• Thinking evaluation changes model structure
• Believing evaluation increases data size

2. Which of the following is the correct way to evaluate a fine-tuned model in Python using TensorFlow?

easy

A. model.compile(optimizer='adam')

B. model.train(test_data, test_labels)

C. model.predict(train_data)

D. model.evaluate(test_data, test_labels)

Solution

1. Step 1: Recall TensorFlow evaluation method

   TensorFlow models use model.evaluate() to measure performance on test data.

2. Step 2: Identify correct usage

   model.evaluate(test_data, test_labels) returns loss and metrics on unseen data.

3. Final Answer:

   model.evaluate(test_data, test_labels) -> Option D

4. Quick Check:

   Use model.evaluate() for testing [OK]

Hint: Use model.evaluate() with test data for evaluation [OK]

Common Mistakes:

• Using model.train() instead of evaluate
• Calling predict() without labels for evaluation
• Confusing compile() with evaluation

3. Given the code below, what will be the output of print(loss, accuracy)?

loss, accuracy = model.evaluate(x_test, y_test)
print(loss, accuracy)

medium

A. The loss value and accuracy metric on the test set

B. The training loss and accuracy values

C. A syntax error because evaluate returns only one value

D. The predicted labels for x_test

Solution

1. Step 1: Understand model.evaluate() output

   It returns loss and metrics (like accuracy) on the test data.

2. Step 2: Analyze the print statement

   Printing loss, accuracy shows these two values from evaluation.

3. Final Answer:

   The loss value and accuracy metric on the test set -> Option A

4. Quick Check:

   evaluate() returns loss and accuracy [OK]

Hint: model.evaluate() returns loss and metrics tuple [OK]

Common Mistakes:

• Thinking evaluate returns training metrics
• Assuming evaluate returns predictions
• Believing evaluate returns only one value

4. You ran model.evaluate(x_test) but got an error. What is the likely cause?

medium

A. The model is not compiled

B. The test data x_test is empty

C. Missing the true labels y_test in the evaluate call

D. The model has too many layers

Solution

1. Step 1: Check evaluate method requirements

   model.evaluate() needs both input data and true labels to compute metrics.

2. Step 2: Identify missing argument

   Calling model.evaluate(x_test) misses y_test, causing an error.

3. Final Answer:

   Missing the true labels y_test in the evaluate call -> Option C

4. Quick Check:

   evaluate() needs inputs and labels [OK]

Hint: Always pass both data and labels to evaluate() [OK]

Common Mistakes:

• Forgetting to pass labels to evaluate()
• Assuming evaluate works with inputs only
• Ignoring model compilation status

5. You fine-tuned two models and got these evaluation results on the same test set:

• Model A: loss=0.25, accuracy=0.90
• Model B: loss=0.20, accuracy=0.85

Which model should you choose and why?

hard

A. Model A, because it has higher accuracy which is more important than loss

B. Model B, because it has lower loss indicating better overall fit

C. Model A, because loss and accuracy must both be higher

D. Model B, because accuracy is less important than loss

Solution

1. Step 1: Understand evaluation metrics

   Accuracy shows correct predictions percentage; loss shows error magnitude.

2. Step 2: Compare models on accuracy and loss

   Model A has higher accuracy (0.90) but slightly higher loss (0.25) than Model B.

3. Step 3: Decide based on goal

   For classification, accuracy is usually more important to pick the better model.

4. Final Answer:

   Model A, because it has higher accuracy which is more important than loss -> Option A

5. Quick Check:

   Higher accuracy preferred for classification [OK]

Hint: Pick model with higher accuracy for classification tasks [OK]

Common Mistakes:

• Choosing model with lower loss but worse accuracy
• Ignoring accuracy when loss differs
• Assuming loss always trumps accuracy