typos

Author: pagezyhf

docs/sagemaker/source/tutorials/jumpstart/jumpstart-quickstart.md

@@ -2,14 +2,14 @@
 
 ## Why use SageMaker JumpStart for Hugging Face models?
 
-Amazon SageMaker **JumpStart** lets you deploy the most-popular open Hugging Face models with **one click**—inside your own AWS account. JumpStart offers a curated [selection(https://aws.amazon.com/sagemaker-ai/jumpstart/getting-started/?sagemaker-jumpstart-cards.sort-by=item.additionalFields.model-name&sagemaker-jumpstart-cards.sort-order=asc&awsf.sagemaker-jumpstart-filter-product-type=*all&awsf.sagemaker-jumpstart-filter-text=*all&awsf.sagemaker-jumpstart-filter-vision=*all&awsf.sagemaker-jumpstart-filter-tabular=*all&awsf.sagemaker-jumpstart-filter-audio-tasks=*all&awsf.sagemaker-jumpstart-filter-multimodal=*all&awsf.sagemaker-jumpstart-filter-RL=*all&awsm.page-sagemaker-jumpstart-cards=1&sagemaker-jumpstart-cards.q=qwen&sagemaker-jumpstart-cards.q_operator=AND)] of model checkpoints for various tasks, including text generation, embeddings, vision, audio, and more. Most models are deployed using the official [Hugging Face Deep Learning Containers](https://huggingface.co/docs/sagemaker/main/en/dlcs/introduction) with a sensible default instance type, so you can move from idea to production in minutes.
+Amazon SageMaker JumpStart lets you deploy the most-popular open Hugging Face models with one click—inside your own AWS account. JumpStart offers a curated [selection](https://aws.amazon.com/sagemaker-ai/jumpstart/getting-started/?sagemaker-jumpstart-cards.sort-by=item.additionalFields.model-name&sagemaker-jumpstart-cards.sort-order=asc&awsf.sagemaker-jumpstart-filter-product-type=*all&awsf.sagemaker-jumpstart-filter-text=*all&awsf.sagemaker-jumpstart-filter-vision=*all&awsf.sagemaker-jumpstart-filter-tabular=*all&awsf.sagemaker-jumpstart-filter-audio-tasks=*all&awsf.sagemaker-jumpstart-filter-multimodal=*all&awsf.sagemaker-jumpstart-filter-RL=*all&awsm.page-sagemaker-jumpstart-cards=1&sagemaker-jumpstart-cards.q=qwen&sagemaker-jumpstart-cards.q_operator=AND) of model checkpoints for various tasks, including text generation, embeddings, vision, audio, and more. Most models are deployed using the official [Hugging Face Deep Learning Containers](https://huggingface.co/docs/sagemaker/main/en/dlcs/introduction) with a sensible default instance type, so you can move from idea to production in minutes.
 
 In this quickstart guide, we will deploy [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct).
 
 ## 1. Prerequisites
 
-|   | Requirement | Notes |
-|---|-------------|-------|
+|   | Requirement |
+|---|-------------|
 | AWS account with SageMaker enabled | An AWS account that will contain all your AWS resources. |
 | An IAM role to access SageMaker AI | Learn more about how IAM works with SageMaker AI in this [guide](https://docs.aws.amazon.com/sagemaker/latest/dg/security-iam.html). |
 | SageMaker Studio domain and user profile | We recommend using SageMaker Studio for straightforward deployment and inference. Follow this [guide](https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html). |
@@ -18,14 +18,14 @@ In this quickstart guide, we will deploy [Qwen/Qwen2.5-14B-Instruct](https://hug
 ## 2· Endpoint deployment
 
 Let's explain how you would deploy a Hugging Face model to SageMaker browsing through the Jumpstart catalog:
-1. **Open** SageMaker → **JumpStart**.  
-2. Filter **“Hugging Face”** or search for your model (e.g. **Qwen2.5-14B**).  
-3. Click **Deploy** → (optional) adjust instance size / count → **Deploy**.  
-4. Wait until *Endpoints* shows **In service**.  
-5. Copy the **Endpoint name** (or ARN) for later use.
+1. Open SageMaker → JumpStart.  
+2. Filter “Hugging Face” or search for your model (e.g. Qwen2.5-14B).  
+3. Click Deploy → (optional) adjust instance size / count → Deploy.  
+4. Wait until Endpoints shows In service.  
+5. Copy the Endpoint name (or ARN) for later use.
 
 Alternatively, you can also browse through the Hugging Face Model Hub:
-1. Open the model page → Click **Deploy** → **SageMaker** → **Jumpstart** tab if model is available.
+1. Open the model page → Click Deploy → SageMaker → Jumpstart tab if model is available.
 2. Copy the code snippet and use it from a SageMaker Notebook instance.
 
 ```python