Add note about pytorch checkpoints

README.rst

@@ -109,18 +109,26 @@ Pretrained models
 Along with the training code, we release a number of pretrained models for
 different datasets, norms and ε-train values. This list will be updated as
 we release more or improved models. *Please cite this library (see bibtex
-entry below) if you use these models in your research.*
+entry below) if you use these models in your research.* 
 
 For each (model, ε-test) combination we evaluate 20-step and 100-step PGD with a
 step size of `2.5 * ε-test / num_steps`. Since these two accuracies are quite 
 close to each other, we do not consider more steps of PGD.
 For each value of ε-test, we highlight the best robust accuracy achieved over
 different ε-train in bold.
 
-(Note that we did not perform any hyperparameter tuning and simply used the same
+**Note #1**: We did not perform any hyperparameter tuning and simply used the same
 hyperparameters as standard training. It is likely that exploring different 
 training hyperparameters will increasse these robust accuracies by a few percent
-points.)
+points.
+
+**Note #2**: The pytorch checkpoint (``.pt``) files below were saved with the following versions of PyTorch and Dill:
+
+.. code-block::
+
+  torch==1.1.0
+  dill==0.2.9
+
 
 CIFAR10 L2-norm (ResNet50):