this approach we should have exceptions:
All tests that need to download a heavy set of weights or a dataset that is larger than ~50MB (e.g., model or
  tokenizer integration tests, pipeline integration tests) should be set to slow. If you're adding a new model, you
  should create and upload to the hub a tiny version of it (with random weights) for integration tests. This is
  discussed in the following paragraphs.
All tests that need to do a training not specifically optimized to be fast should be set to slow.
We can introduce exceptions if some of these should-be-non-slow tests are excruciatingly slow, and set them to
  @slow. Auto-modeling tests, which save and load large files to disk, are a good example of tests that are marked
  as @slow.
If a test completes under 1 second on CI (including downloads if any) then it should be a normal test regardless.
