Update 2020-7-20-pytorch-1.6-released.md

Author: brucejlin1

_posts/2020-7-20-pytorch-1.6-released.md

@@ -194,13 +194,17 @@ The [2020 CVPR Low-Power Vision Challenge (LPCV) - Online Track for UAV video](h
 
 To reiterate, Prototype features in PyTorch are early features that we are looking to gather feedback on, gauge the usefulness of and improve ahead of graduating them to Beta or Stable. The following features are not part of the PyTorch 1.6 release and instead are available in nightlies with separate docs/tutorials to help facilitate early usage and feedback. 
 
-**Distributed RPC/Profiler** - Allow users to profile training jobs that use `torch.distributed.rpc` using the autograd profiler, and remotely invoke the profiler in order to collect profiling information across different nodes. The RFC can be found [here](https://github.com/pytorch/pytorch/issues/39675) and a short recipe on how to use this feature can be found [here](https://github.com/pytorch/tutorials/tree/master/prototype_source).
+#### Distributed RPC/Profiler
+Allow users to profile training jobs that use `torch.distributed.rpc` using the autograd profiler, and remotely invoke the profiler in order to collect profiling information across different nodes. The RFC can be found [here](https://github.com/pytorch/pytorch/issues/39675) and a short recipe on how to use this feature can be found [here](https://github.com/pytorch/tutorials/tree/master/prototype_source).
 
-**TorchScript Module Freezing:** Module Freezing is the process of inlining module parameters and attributes values into the TorchScript internal representation. Parameter and attribute values are treated as final value and they cannot be modified in the frozen module. The PR for this feature can be found [here](https://github.com/pytorch/pytorch/pull/32178) and a short tutorial on how to use this feature can be found [here](https://github.com/pytorch/tutorials/tree/master/prototype_source).
+#### TorchScript Module Freezing
+Module Freezing is the process of inlining module parameters and attributes values into the TorchScript internal representation. Parameter and attribute values are treated as final value and they cannot be modified in the frozen module. The PR for this feature can be found [here](https://github.com/pytorch/pytorch/pull/32178) and a short tutorial on how to use this feature can be found [here](https://github.com/pytorch/tutorials/tree/master/prototype_source).
 
-**Graph Mode Quantization:** Eager mode quantization requires users to make changes to their model, including explicitly quantizing activations, module fusion, rewriting use of torch ops with Functional Modules and quantization of functionals are not supported. If we can trace or script the model, then the quantization can be done automatically with graph mode quantization without any of the complexities in eager mode, and it is configurable through a `qconfig_dict`. A tutorial on how to use this feature can be found [here](https://github.com/pytorch/tutorials/tree/master/prototype_source).
+#### Graph Mode Quantization
+Eager mode quantization requires users to make changes to their model, including explicitly quantizing activations, module fusion, rewriting use of torch ops with Functional Modules and quantization of functionals are not supported. If we can trace or script the model, then the quantization can be done automatically with graph mode quantization without any of the complexities in eager mode, and it is configurable through a `qconfig_dict`. A tutorial on how to use this feature can be found [here](https://github.com/pytorch/tutorials/tree/master/prototype_source).
 
-**Quantization Numerical Suite:** Quantization is good when it works, but it’s difficult to know what's wrong when it doesn't satisfy the expected accuracy. A prototype is now available for a Numerical Suite that measures comparison statistics between quantized modules and float modules. This is available to test using eager mode and on CPU only with more support coming. A tutorial on how to use this feature can be found [here](https://github.com/pytorch/tutorials/tree/master/prototype_source).
+#### Quantization Numerical Suite
+Quantization is good when it works, but it’s difficult to know what's wrong when it doesn't satisfy the expected accuracy. A prototype is now available for a Numerical Suite that measures comparison statistics between quantized modules and float modules. This is available to test using eager mode and on CPU only with more support coming. A tutorial on how to use this feature can be found [here](https://github.com/pytorch/tutorials/tree/master/prototype_source).
 
 
 Cheers!