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.
Unit tests don’t catch ML failures.Integration tests are slow.Edge cases slip through.I need better confidence.
The batch prediction job used to run in minutes.As data volume increased, runtime started doubling unexpectedly.Nothing changed in the model code itself.Now it’s becoming a bottleneck in the pipeline.
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.
worsen performance on old data
fluctuation in accuracy during training runs
I have a new model ready to deploy.I’m confident in offline metrics, but production risk worries me.A full replacement feels dangerous. What’s the safest approach?