Evaluating fine-tuned models: overfitting, forgetting and benchmarks
Training a model is easy. Knowing when to stop, detecting that it is memorising instead of learning, and verifying that your specialisation did not break general capabilities — these are the hard parts. This lesson covers the diagnostic tools that tell you whether your fine-tuning is working, going too far, or silently degrading the model.
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