README.md in torch-rb-0.2.0 vs README.md in torch-rb-0.2.1

@@ -281,10 +281,26 @@
 loss = criterion.call(output, target)
 loss.backward
 optimizer.step
 ```
 
+### Saving and Loading Models
+
+Save a model
+
+```ruby
+Torch.save(net.state_dict, "net.pth")
+```
+
+Load a model
+
+```ruby
+net = Net.new
+net.load_state_dict(Torch.load("net.pth"))
+net.eval
+```
+
 ### Tensor Creation
 
 Here’s a list of functions to create tensors (descriptions from the [C++ docs](https://pytorch.org/cppdocs/notes/tensor_creation.html)):
 
 - `arange` returns a tensor with a sequence of integers
@@ -442,9 +458,11 @@
 cd torch.rb
 bundle install
 bundle exec rake compile -- --with-torch-dir=/path/to/libtorch
 bundle exec rake test
 ```
+
+You can use [this script](https://gist.github.com/ankane/9b2b5fcbd66d6e4ccfeb9d73e529abe7) to test on GPUs with the AWS Deep Learning Base AMI (Ubuntu 18.04).
 
 Here are some good resources for contributors:
 
 - [PyTorch API](https://pytorch.org/docs/stable/torch.html)
 - [PyTorch C++ API](https://pytorch.org/cppdocs/)