Evaluating Fine-Tuned Models Effectively
Evaluating Fine-Tuned Models Effectively
Fine-tuning must be validated carefully. Improvement must be measurable.
1) Metrics to Track
- Accuracy
- Loss reduction
- Response consistency
2) Overfitting Detection
If validation performance drops, model may be memorizing instead of learning.
3) Human Evaluation
For generative tasks, manual review remains critical.
4) Summary
Evaluation ensures fine-tuning actually adds value.
