Update models-guide.md

grammar nits

docs/models-guide.md

@@ -17,18 +17,18 @@ limitations under the License.
 # Sparsify Models Guide
 
 For any Sparsify Experiments, a dense model can be supplied for sparsification.
-One Shot is the only experiment type that requires a model to be passed in.
+One-Shot is the only experiment type that requires a model to be passed in.
 For others, a default model will be chosen to best fit the given use case.
-Due to the varied ML pipelines and implementations, Sparsify standardizes on a few, popular formats for models.
+Due to the varied ML pipelines and implementations, Sparsify standardizes on a few popular formats for models.
 You will need to make sure that your models are formatted properly according to the standards listed below.
 
-## One Shot
+## One-Shot
 
-The ONNX model format is the only currently supported one for one shot.
-See the SparseML documentation for exporting to ONNX formats.
-In the near future, more formats will be added for support with one shot.
+The ONNX model format is the only currently supported format for One-Shot.
+See the [SparseML documentation](https://docs.neuralmagic.com) for exporting to ONNX formats.
+In the near future, more formats will be added for support with One-Shot.
 
-## Training Aware and Sparse Transfer
+## Training-Aware and Sparse-Transfer
 
-The PyTorch model format is the only currently supported one for training aware and sparse transfer experiments.
+The PyTorch model format is the only currently supported format for Training-Aware and Sparse-Transfer Experiments.
 The exact format will depend on the pipeline, and therefore the use case, for the experiment.