It _is_ deterministic (same input gives same output). You typically don't "test" pairs of inputs/outputs for a model. Instead you measure its performance by de

It _is_ deterministic (same input gives same output). You typically don't "test" pairs of inputs/outputs for a model. Instead you measure its performance by defining metrics e.g. "what's the ROUGE-2 score on summarization after fine-tuning AlexaTM 20B using N examples from dataset Y" You can test some aspects of ML models, like sync testing (if you train on hardware A and run on hardware B, their results are not always the same). But generally you test the code that embeds the model, not the model itself.