jumpstart guide

Author: pagezyhf

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

@@ -1,3 +1,86 @@
-# Jumpstart Quickstart
+# Quickstart - Deploy Hugging Face Models with SageMaker Jumpstart
 
-This page is under construction, bear with us!
+## 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.
+
+In this quickstart guide, we will deploy [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct).
+
+## 1. Prerequisites
+
+|   | Requirement | Notes |
+|---|-------------|-------|
+| 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). |
+| Service quotas | Most LLMs need GPU instances (e.g. ml.g5). Verify you have quota for ml.g5.24xlarge or [request it](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-requesting-quota-increases.html). | 
+
+## 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.
+
+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.
+2. Copy the code snippet and use it from a SageMaker Notebook instance.
+
+```python
+# SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
+
+from sagemaker.jumpstart.model import JumpStartModel
+
+
+model = JumpStartModel(model_id="huggingface-llm-qwen2-5-14b-instruct")
+example_payloads = model.retrieve_all_examples()
+
+predictor = model.deploy()
+
+for payload in example_payloads:
+    response = predictor.predict(payload.body)
+    print("Input:\n", payload.body[payload.prompt_key])
+    print("Output:\n", response[0]["generated_text"], "\n\n===============\n")
+```
+
+The endpoint creation can take several minutes, depending on the size of the model.
+
+## 3. Test interactively
+
+If you deployed through the console, you need to grab the endpoint ARN and reuse in your code.
+```python
+from sagemaker.predictor import retrieve_default
+endpoint_name = "MY ENDPOINT NAME"
+predictor = retrieve_default(endpoint_name)
+payload = {
+    "messages": [
+        {
+            "role": "system",
+            "content": "You are a passionate data scientist."
+        },
+        {
+            "role": "user",
+            "content": "what is machine learning?"
+        }
+    ],
+    "max_tokens": 2048,
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "stream": False
+}
+
+response = predictor.predict(payload)
+print(response)
+```
+
+The endpoint support the Open AI API specification. 
+
+## 4. Clean‑up
+
+To avoid incurring unnecessary costs, when you’re done, delete the SageMaker endpoints in the Deployments → Endpoints console or using the following code snippets:
+```python
+predictor.delete_model()
+predictor.delete_endpoint()
+```