A fine-tuning run exceeds GPU memory limits. Which two changes can reduce training memory requirements while keeping adaptation feasible? Select 2 answers.

1. Increase the number of optimizer states per trainable parameter.
2. Keep every intermediate tensor permanently for debugging.
3. Reduce the per-device microbatch size.
4. Double the sequence length for every example.
5. Use a parameter-efficient adapter method supported by the model.