I fine-tuned a Transformer model without any memory issues.But when I call model.generate(), CUDA runs out of memory.This happens even for short prompts.Training worked fine, so this feels confusing.
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I added thousands of new user interactions to my training dataset.Instead of improving, the recommendation quality dropped.Users are now getting irrelevant suggestions.It feels like more data made the model less accurate.
Feature distributions look stable.But prediction quality is declining.Simple drift metrics don’t explain it.Something deeper seems wrong.
The same IAM policy works perfectly in my test environment.Once deployed to production, access starts failing without any obvious policy changes.I’m trying to understand why permissions behave differently and how to troubleshoot this safely.
