diff --git a/docs/samples/v1beta1/torchserve/README.md b/docs/samples/v1beta1/torchserve/README.md
index fde19b472e0..43af90dbbe7 100644
--- a/docs/samples/v1beta1/torchserve/README.md
+++ b/docs/samples/v1beta1/torchserve/README.md
@@ -1,17 +1,17 @@
 # Predict on a InferenceService using TorchServe
 
-In this example, we use a trained pytorch mnist model to predict handwritten digits by running an inference service with [TorchServe](https://github.com/pytorch/serve) predictor.
+In this example, we use a trained PyTorch MNIST model to predict handwritten digits by running an inference service with [TorchServe](https://github.com/pytorch/serve) predictor.
 
 ## Setup
 
-1. Your ~/.kube/config should point to a cluster with [KServe installed](https://github.com/kserve/kserve#installation)
+1. Your ~/.kube/config should point to a cluster with [KServe installed](https://github.com/kserve/kserve#installation).
 2. Your cluster's Istio Ingress gateway must be [network accessible](https://istio.io/latest/docs/tasks/traffic-management/ingress/ingress-control/).
 
-## Creating model storage with model archive file
+## Creating Model Storage with a Model Archive File
 
 TorchServe provides a utility to package all the model artifacts into a single [Torchserve Model Archive Files (MAR)](https://github.com/pytorch/serve/blob/master/model-archiver/README.md).
 
-You can store your model and dependent files on remote storage or local persistent volume, the mnist model and dependent files can be obtained
+You can store your model and dependent files on remote storage or local persistent volume, the MNIST model and dependent files can be obtained
 from [here](https://github.com/pytorch/serve/tree/master/examples/image_classifier/mnist).
 
 The KServe/TorchServe integration expects following model store layout.
@@ -24,27 +24,21 @@ The KServe/TorchServe integration expects following model store layout.
 │   ├── mnist.mar
 ```
 
-- For remote storage you can choose to start the example using the prebuilt mnist MAR file stored on KServe example GCS bucket
-`gs://kfserving-examples/models/torchserve/image_classifier`,
-you can also generate the MAR file with `torch-model-archiver` and create the model store on remote storage according to the above layout.
+> **Note**: 
+> For remote storage you can choose to start the example using the prebuilt MNIST MAR file stored on KServe example GCS bucket `gs://kfserving-examples/models/torchserve/image_classifier`, or generate the MAR file with `torch-model-archiver` and create the model store on remote storage according to the above layout.
+>   ```bash
+>    torch-model-archiver --model-name mnist --version 1.0 \
+>    --model-file model-archiver/model-store/mnist/mnist.py \
+>    --serialized-file model-archiver/model-store/mnist/mnist_cnn.pt \
+>    --handler model-archiver/model-store/mnist/mnist_handler.py \
+>    ```
+>    For PVC user please refer to [model archive file generation](./model-archiver/README.md) for auto generation of MAR files with the model and dependent files.
 
-```bash
-torch-model-archiver --model-name mnist --version 1.0 \
---model-file model-archiver/model-store/mnist/mnist.py \
---serialized-file model-archiver/model-store/mnist/mnist_cnn.pt \
---handler model-archiver/model-store/mnist/mnist_handler.py
-```
-
-
-- For PVC user please refer to [model archive file generation](./model-archiver/README.md) for auto generation of MAR files from
-the model and dependent files.
-
-The requests are converted from KServe inference request format to torchserve request format and sent to the `inference_address` configured
-via local socket.
+The requests are converted from KServe inference request format to torchserve request format and sent to the `inference_address` configured via local socket.
 
 **Warning**: The `service_envelope` property has beed dreprecated and replaced with `enable_envvars_config` set to true. This enables the service envelope to be set on runtime.
 
-## TorchServe with KFS envelope inference endpoints
+## TorchServe with KFS Envelope Inference Endpoints
 
 The KServe/TorchServe integration supports both KServe v1 and v2 protocols.
 
@@ -62,13 +56,12 @@ V2 Protocol
 | Predict  | POST  | /v2/models/<model_name>/infer  | Request: {"id" : $string #optional,"parameters" : $parameters #optional,"inputs" : [ $request_input, ... ],"outputs" : [ $request_output, ... ] #optional}  Response:{"model_name" : $string,"model_version" : $string #optional,"id" : $string,"parameters" : $parameters #optional,"outputs" : [ $response_output, ... ]}
 | Explain  | POST  | /v2/models/<model_name>/explain  | Request: {"id" : $string #optional,"parameters" : $parameters #optional,"inputs" : [ $request_input, ... ],"outputs" : [ $request_output, ... ] #optional}  Response:{"model_name" : $string,"model_version" : $string #optional,"id" : $string,"parameters" : $parameters #optional,"outputs" : [ $response_output, ... ]} ||
 
-[Sample requests for text and image classification](https://github.com/pytorch/serve/tree/master/kubernetes/kserve/kf_request_json)
+[Sample Requests for Text and Image Classification](https://github.com/pytorch/serve/tree/master/kubernetes/kserve/kf_request_json)
 
 ## Autoscaling
 
 One of the main serverless inference features is to automatically scale the replicas of an `InferenceService` matching the incoming workload.
-KServe by default enables [Knative Pod Autoscaler](https://knative.dev/docs/serving/autoscaling/) which watches traffic flow and scales up and down
-based on the configured metrics.
+KServe by default enables [Knative Pod Autoscaler](https://knative.dev/docs/serving/autoscaling/) which watches traffic flow and scales up and down based on the configured metrics.
 
 [Autoscaling Example](autoscaling/README.md)
 
@@ -80,4 +73,4 @@ Canary rollout is a deployment strategy when you release a new version of model
 
 ## Monitoring
 
-[Expose metrics and setup grafana dashboards](metrics/README.md)
+[Metrics Exposure and Grafana Dashboard Setup](metrics/README.md)