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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My language model produces fluent responses.Even when it does not know the answer, it sounds confident.Users sometimes trust incorrect replies.There is no indication of uncertainty.
The training loss drops steadily during fine-tuning.But the translated sentences are grammatically wrong.BLEU and other quality metrics do not improve.It feels like the model is optimizing the wrong thing.
My GAN generates images but they look washed out.Many samples look almost identical.Training loss looks stable.But the visual quality never improves.
The agent performs well in simulation.When deployed in the real world, it makes strange decisions.The physics is slightly different.Small changes lead to big failures.
My diagnostic CNN shows high accuracy on data from one hospital.When tested on scans from a different hospital, performance drops drastically.The disease patterns are the same.Only the scanners and imaging pipelines differ.
My GAN generates faces.But many look distorted or unnatural.Eyes and mouths appear in wrong positions.The training seems stable, but outputs are flawed.
The model produces grammatically correct text.But it keeps repeating the same phrases.The output never moves forward.It feels stuck in a loop?
My model gives great accuracy on my laptop.When deployed on a server, predictions become inconsistent.The same input sometimes produces different outputs.Nothing crashes, but the behavior is unreliable.
The model classifies upright images correctly.Rotated versions of the same images fail.The content is the same.Only orientation changed.