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

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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

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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

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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

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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.