A model fine-tuned on Amazon Bedrock might not show significant improvement for several reasons. First, the fine-tuning dataset may not align well with the model’s original training data or the target task. For example, if the dataset is too small, lacks diversity, or contains noisy labels, the model may struggle to generalize. If you’re training a model for medical text analysis but your dataset includes informal social media posts, the mismatch could limit improvement. Second, hyperparameter choices—like learning rate, batch size, or number of epochs—might not be optimized for the task. A learning rate that’s too high could cause unstable training, while one that’s too low might not allow the model to adapt sufficiently. Finally, the base model itself might already be near its performance ceiling for the task, leaving little room for improvement. For instance, if the base model was pretrained on a broad corpus and your task is simple, fine-tuning may not add much value.
To verify if the fine-tuning dataset was applied correctly, start by checking the training logs and metrics provided by Bedrock. Ensure that the training job completed without errors and that the dataset was ingested properly. Look for confirmation that the correct number of training examples and epochs were processed. Next, test the model on specific examples from your training dataset. For instance, if your dataset includes labeled pairs like “Translate ‘Hello’ to French → ‘Bonjour’,” run inference on those inputs and check if outputs match expectations. If the model performs well on training examples but poorly on validation data, this suggests overfitting rather than a dataset application issue. Additionally, compare the fine-tuned model’s performance against the base model using a held-out test set. If there’s no improvement, it might indicate that the dataset isn’t sufficiently tailored to the task or that the evaluation metrics aren’t sensitive enough to capture subtle changes.
For further troubleshooting, consider auditing the dataset’s quality. Remove duplicates, fix mislabeled entries, and ensure the data distribution matches real-world scenarios. Experiment with smaller subsets of data to see if the model shows incremental improvements, which can help identify scaling issues. For example, if training on 100 examples yields better results than 1,000, your larger dataset might contain irrelevant examples. You could also adjust hyperparameters incrementally—like reducing the learning rate by half—and monitor validation loss for stability. Finally, validate that the task format (e.g., prompt structure for text generation) matches how the model was pretrained. If the base model expects “Question: [text] Answer: [text]” but your fine-tuning data uses a different format, the model might not leverage the data effectively. Addressing these factors systematically can help isolate the root cause of poor performance.