I fine-tuned a pretrained Transformer on a small custom dataset.Training finishes without errors.But the generated outputs look random and off-topic.It feels like the model forgot everything.
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My deployed model isn’t crashing or throwing errors.The API responds normally, but predictions are clearly wrong.There are no obvious logs indicating failure.I’m unsure where to even start debugging.
Traffic is stable.Model architecture hasn’t changed.Yet costs keep rising month over month.It’s hard to explain.
Security fixes often block releases and frustrate developers.Remediation feels disruptive rather than incremental.I’m looking for ways to reduce friction without ignoring security.
transformer model debugging
sudden divergance
