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<br>Today, we are thrilled to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://vibefor.fun)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](http://git.thinkpbx.com) concepts on AWS.<br>
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<br>In this post, we show how to get begun with DeepSeek-R1 on [Amazon Bedrock](https://talentlagoon.com) Marketplace and SageMaker JumpStart. You can [follow comparable](https://git.tasu.ventures) steps to release the distilled variations of the models too.<br>
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<br>Overview of DeepSeek-R1<br>
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<br>DeepSeek-R1 is a big language model (LLM) developed by [DeepSeek](https://bikrikoro.com) [AI](https://www.cittamondoagency.it) that utilizes support learning to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing function is its support knowing (RL) action, which was used to refine the model's responses beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually improving both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's geared up to break down intricate inquiries and factor through them in a detailed way. This guided thinking procedure permits the design to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be [integrated](https://git.alenygam.com) into different workflows such as representatives, logical reasoning and information interpretation jobs.<br>
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, enabling efficient reasoning by routing inquiries to the most pertinent expert "clusters." This technique permits the model to concentrate on different issue domains while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, [surgiteams.com](https://surgiteams.com/index.php/User:LatanyaZiegler) and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, [surgiteams.com](https://surgiteams.com/index.php/User:Mirta17E66502287) more efficient models to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.<br>
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<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and [assess designs](http://103.140.54.203000) against key security criteria. At the time of this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://git.runsimon.com) applications.<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation boost, create a limitation increase demand and connect to your account team.<br>
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<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to use guardrails for content filtering.<br>
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<br>Implementing guardrails with the ApplyGuardrail API<br>
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<br>Amazon Bedrock Guardrails permits you to introduce safeguards, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:LawerenceJeanner) avoid harmful content, and assess designs against key safety criteria. You can implement safety procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.<br>
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<br>The general flow involves the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or [output phase](http://195.58.37.180). The examples showcased in the following sections demonstrate reasoning using this API.<br>
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
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<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To [gain access](https://1samdigitalvision.com) to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<br>
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<br>1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane.
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At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for [DeepSeek](http://103.77.166.1983000) as a service provider and choose the DeepSeek-R1 design.<br>
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<br>The model detail page offers important details about the design's capabilities, prices structure, and application guidelines. You can discover detailed usage directions, consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, including material creation, code generation, and concern answering, using its support finding out optimization and CoT thinking capabilities.
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The page also consists of release alternatives and licensing details to help you start with DeepSeek-R1 in your applications.
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3. To [start utilizing](https://gitlab-heg.sh1.hidora.com) DeepSeek-R1, choose Deploy.<br>
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<br>You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
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4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
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5. For Number of circumstances, enter a number of circumstances (in between 1-100).
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6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a [GPU-based](https://gitea.moerks.dk) circumstances type like ml.p5e.48 xlarge is recommended.
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Optionally, you can set up advanced security and [facilities](https://starttrainingfirstaid.com.au) settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you might desire to examine these settings to line up with your company's security and compliance requirements.
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7. Choose Deploy to start using the design.<br>
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<br>When the release is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground.
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8. Choose Open in play area to access an interactive user interface where you can explore various triggers and adjust design parameters like temperature and maximum length.
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For example, content for reasoning.<br>
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<br>This is an excellent method to check out the model's thinking and text generation capabilities before incorporating it into your applications. The play ground provides [instant](https://dainiknews.com) feedback, assisting you understand how the [model reacts](https://losangelesgalaxyfansclub.com) to different inputs and letting you fine-tune your prompts for optimum outcomes.<br>
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<br>You can rapidly check the model in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you [require](http://115.29.202.2468888) to get the endpoint ARN.<br>
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<br>Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to perform inference using a [released](http://docker.clhero.fun3000) DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends out a request to generate text based on a user timely.<br>
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<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
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<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can release with simply a couple of clicks. With [SageMaker](http://lty.co.kr) JumpStart, you can [tailor pre-trained](http://40th.jiuzhai.com) models to your usage case, with your data, and deploy them into production using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical methods: utilizing the instinctive SageMaker JumpStart UI or carrying out [programmatically](https://thankguard.com) through the SageMaker Python SDK. Let's check out both methods to help you choose the method that best matches your needs.<br>
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
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<br>Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, choose Studio in the navigation pane.
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2. First-time users will be triggered to create a domain.
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3. On the SageMaker Studio console, pick [JumpStart](https://daeshintravel.com) in the navigation pane.<br>
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<br>The model browser displays available designs, with details like the service provider name and model capabilities.<br>
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
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Each design card shows essential details, consisting of:<br>
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<br>- Model name
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- Provider name
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- Task category (for example, Text Generation).
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Bedrock Ready badge (if suitable), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model<br>
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<br>5. Choose the design card to see the design details page.<br>
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<br>The design details page consists of the following details:<br>
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<br>- The design name and supplier details.
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Deploy button to deploy the model.
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About and Notebooks tabs with detailed details<br>
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<br>The About tab includes essential details, such as:<br>
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<br>- Model description.
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- License details.
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[- Technical](https://titikaka.unap.edu.pe) requirements.
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- Usage guidelines<br>
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<br>Before you deploy the design, it's recommended to evaluate the [model details](http://code.exploring.cn) and license terms to [confirm compatibility](https://scode.unisza.edu.my) with your usage case.<br>
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<br>6. Choose Deploy to proceed with release.<br>
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<br>7. For Endpoint name, use the immediately generated name or create a custom one.
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8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial instance count, go into the variety of instances (default: 1).
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Selecting appropriate circumstances types and counts is vital for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
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10. Review all setups for precision. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
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11. Choose Deploy to release the model.<br>
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<br>The deployment procedure can take numerous minutes to complete.<br>
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<br>When release is complete, your endpoint status will alter to InService. At this point, the model is ready to accept inference demands through the [endpoint](http://139.9.60.29). You can monitor the implementation progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your applications.<br>
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
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<br>To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for [releasing](http://47.93.234.49) the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.<br>
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<br>You can run additional demands against the predictor:<br>
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<br>Implement guardrails and run inference with your SageMaker JumpStart predictor<br>
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<br>Similar to Amazon Bedrock, you can also use the [ApplyGuardrail API](http://wp10476777.server-he.de) with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
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<br>Tidy up<br>
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<br>To avoid undesirable charges, finish the actions in this section to clean up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace implementation<br>
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<br>If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:<br>
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<br>1. On the Amazon [Bedrock](https://actv.1tv.hk) console, under Foundation designs in the navigation pane, pick Marketplace [implementations](https://members.mcafeeinstitute.com).
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2. In the Managed [deployments](https://croart.net) section, find the endpoint you desire to erase.
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3. Select the endpoint, and on the Actions menu, select Delete.
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4. Verify the endpoint details to make certain you're deleting the appropriate implementation: 1. Endpoint name.
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2. Model name.
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3. Endpoint status<br>
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<br>Delete the SageMaker JumpStart predictor<br>
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<br>The [SageMaker](https://powerstack.co.in) JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
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<br>Conclusion<br>
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<br>In this post, we explored how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, [Amazon SageMaker](http://git.thinkpbx.com) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.<br>
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<br>About the Authors<br>
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<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://surreycreepcatchers.ca) companies build innovative services utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference efficiency of big language models. In his downtime, Vivek enjoys treking, enjoying films, and [attempting](http://otyjob.com) various foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://aggeliesellada.gr) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://www.munianiagencyltd.co.ke) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [Bioinformatics](http://120.77.67.22383).<br>
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<br>Jonathan Evans is an Expert Solutions Architect [dealing](https://www.dailynaukri.pk) with generative [AI](https://vidacibernetica.com) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://bikapsul.com) center. She is enthusiastic about developing solutions that assist customers accelerate their [AI](http://114.132.245.203:8001) journey and unlock organization value.<br>
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