Why a Fine-Tuned Model on Bedrock Might Not Show Improvement A fine-tuned model on AWS Bedrock might not show significant improvement for several reasons. First, the dataset used for fine-tuning might be insufficient in size, diversity, or relevance. For example, if the dataset is too small, the model may not learn meaningful patterns, or if it lacks examples of edge cases, the model’s generalization could suffer. Second, the base model’s architecture or pretraining might not align with your task. If the original model was trained for generic text generation but your task requires structured outputs (e.g., JSON formatting), fine-tuning alone may not bridge the gap. Third, hyperparameter choices (e.g., learning rate, batch size) during fine-tuning might be suboptimal, leading to unstable training or underfitting.
Verifying Dataset Application To verify if your dataset was applied correctly, start by auditing the data preprocessing steps. Ensure the dataset was formatted according to Bedrock’s requirements (e.g., correct JSON structure, proper tokenization). Check for data leakage by confirming that validation/test sets weren’t accidentally included in training. Use Bedrock’s training logs to validate that the expected number of examples were processed and that no errors occurred during ingestion. For example, if you provided 1,000 examples but the logs show only 800 processed, there may have been formatting issues. Additionally, perform a sanity check by fine-tuning on a small subset of data and verifying if the model overfits—if performance on the subset improves but not on the full dataset, the issue likely lies in scalability or data quality.
Next Steps for Diagnosis If the dataset was applied correctly, evaluate the model’s performance using task-specific metrics. For instance, if your task involves classification, compute precision/recall instead of relying solely on loss values. Compare the fine-tuned model’s outputs against the base model’s outputs for the same inputs to identify qualitative improvements. Use Bedrock’s model evaluation tools (or custom scripts) to test on a held-out dataset. If results remain poor, revisit the dataset: augment it with more diverse examples, balance class distributions, or clean mislabeled data. Experiment with hyperparameters (e.g., reduce the learning rate if training is unstable) or try a different base model better suited to your task (e.g., choose a model pretrained on domain-specific data). Finally, ensure the fine-tuning pipeline itself isn’t misconfigured—for example, confirm that the correct model version and training script are being used.