I’ve currently written a model that works on float32. However, I’d like to try mixed precision training due to uses not having enough memory. However, after following this page: https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html, and adding the grad scaler and with autocast, I noticed that no memory was being saved, and when checking params within the autocast while section of my code, I noticed it was all fp32. Are there any other steps I need to take to modify the model code to work with mixed precision, and if necessary, could you link resources with steps on how to modify my model?