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<br>Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.sortug.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](https://git.o-for.net) concepts on AWS.<br>
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<br>In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs also.<br>
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<br>Overview of DeepSeek-R1<br>
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<br>DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://dubai.risqueteam.com) that uses reinforcement learning to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating function is its reinforcement knowing (RL) action, which was [utilized](https://essencialponto.com.br) to refine the design's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually boosting both significance and clarity. In addition, DeepSeek-R1 [employs](https://work-ofie.com) a chain-of-thought (CoT) technique, implying it's equipped to break down intricate queries and reason through them in a detailed manner. This assisted thinking procedure [permits](https://kiaoragastronomiasocial.com) the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, sensible thinking and information analysis jobs.<br>
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:KimCarrion3) is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, [enabling efficient](https://powerstack.co.in) inference by routing [queries](https://earlyyearsjob.com) to the most pertinent expert "clusters." This approach [enables](https://lasvegasibs.ae) the model to focus on various problem domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of [HBM memory](https://thevesti.com) in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher design.<br>
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<br>You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and examine designs against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](https://gitea.star-linear.com) applications.<br>
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<br>Prerequisites<br>
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<br>To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, [pick Amazon](https://gitlab.mnhn.lu) 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 instance in the AWS Region you are deploying. To request a limit increase, create a limitation increase [request](https://frce.de) and reach out to your account team.<br>
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish [consents](https://jobs.theelitejob.com) to utilize guardrails for material filtering.<br>
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<br>Implementing guardrails with the ApplyGuardrail API<br>
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<br>Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous material, and examine designs against crucial safety requirements. You can implement safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using 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 circulation involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or output phase. 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 designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane.
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At the time of composing this post, you can use the InvokeModel API to invoke the design. It doesn't [support Converse](https://git.owlhosting.cloud) APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.<br>
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<br>The design detail page supplies necessary details about the model's abilities, rates structure, and implementation standards. You can discover detailed use directions, consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of content production, code generation, [it-viking.ch](http://it-viking.ch/index.php/User:GeorginaBettis) and question answering, using its reinforcement finding out optimization and CoT thinking capabilities.
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The page likewise consists of deployment choices and licensing details to assist you get started with DeepSeek-R1 in your applications.
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3. To begin using DeepSeek-R1, choose Deploy.<br>
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<br>You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated.
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4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
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5. For Number of instances, enter a number of instances (between 1-100).
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6. For Instance type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based [circumstances type](http://mpowerstaffing.com) like ml.p5e.48 xlarge is recommended.
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Optionally, you can [configure sophisticated](https://78.47.96.1613000) security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may wish to evaluate these settings to align with your organization'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 deployment is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
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8. Choose Open in play ground to access an interactive user interface where you can explore different triggers and adjust model parameters like temperature and maximum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, content for inference.<br>
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<br>This is an outstanding method to check out the model's thinking and text generation abilities before integrating it into your [applications](https://candidates.giftabled.org). The play ground provides [instant](http://112.74.102.696688) feedback, helping you comprehend how the design reacts to various inputs and letting you fine-tune your prompts for optimum results.<br>
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<br>You can rapidly evaluate the model in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
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<br>Run inference utilizing guardrails with the released DeepSeek-R1 endpoint<br>
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<br>The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_ customer, configures reasoning criteria, and sends out a [request](https://moojijobs.com) to generate text based upon 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) center with FMs, built-in algorithms, and prebuilt ML services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 design through SageMaker JumpStart provides two hassle-free techniques: using the intuitive SageMaker JumpStart UI or [implementing programmatically](https://git.eisenwiener.com) through the [SageMaker Python](http://109.195.52.923000) SDK. Let's [explore](http://106.55.3.10520080) both approaches to assist you select the method that best suits your needs.<br>
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
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<br>Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br>
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<br>1. On the SageMaker console, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:KimberleyFilson) pick Studio in the navigation pane.
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2. First-time users will be prompted to develop a domain.
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3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br>
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<br>The design web browser displays available designs, with details like the provider name and model abilities.<br>
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
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Each design card shows essential details, including:<br>
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<br>- Model name
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- Provider name
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- Task classification (for example, Text Generation).
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Bedrock Ready badge (if suitable), indicating that this design can be registered with Amazon Bedrock, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:Tammi64F503957) allowing you to use Amazon Bedrock APIs to invoke the model<br>
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<br>5. Choose the model card to see the model details page.<br>
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<br>The model details page includes the following details:<br>
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<br>- The model name and [company details](https://git.junzimu.com).
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Deploy button to deploy the design.
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About and Notebooks tabs with detailed details<br>
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<br>The About tab consists of essential details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical specs.
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- Usage standards<br>
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<br>Before you release the design, it's suggested to review the model details and license terms to confirm compatibility with your usage case.<br>
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<br>6. Choose Deploy to continue with release.<br>
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<br>7. For Endpoint name, utilize the instantly generated name or produce a custom one.
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8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial instance count, get in the variety of instances (default: 1).
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Selecting suitable instance types and counts is important for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency.
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10. Review all configurations for precision. For this model, we strongly recommend adhering to SageMaker JumpStart default [settings](https://www.buzzgate.net) and making certain that network seclusion remains in place.
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11. Choose Deploy to deploy the model.<br>
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<br>The implementation procedure can take numerous minutes to complete.<br>
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<br>When release is total, your endpoint status will alter to InService. At this point, the model is all set to accept reasoning demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can invoke the design using a SageMaker runtime client and integrate 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 going with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for [inference programmatically](https://git.serenetia.com). The code for [releasing](https://work-ofie.com) 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 extra demands against the predictor:<br>
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<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
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<br>Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart [predictor](https://gitea.ci.apside-top.fr). You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:<br>
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<br>Clean up<br>
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<br>To [prevent unwanted](https://mp3talpykla.com) charges, finish the steps in this section to tidy up your resources.<br>
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<br>Delete the Amazon Bedrock Marketplace release<br>
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<br>If you deployed the design using Amazon Bedrock Marketplace, total the following steps:<br>
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations.
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2. In the Managed deployments area, find the endpoint you wish 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 erasing 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://puzzle.thedimeland.com) JumpStart design you deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you wish 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 release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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](http://advance5.com.my) companies construct ingenious services utilizing AWS services and sped up compute. Currently, he is [focused](https://git.wsyg.mx) on establishing techniques for fine-tuning and optimizing the inference performance of large [language](https://hatchingjobs.com) designs. In his spare time, Vivek delights in hiking, enjoying motion pictures, and trying various foods.<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://demo.playtubescript.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://scm.fornaxian.tech) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
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<br>Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](http://www.xn--2i4bi0gw9ai2d65w.com) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://code.estradiol.cloud) hub. She is enthusiastic about building services that help [customers accelerate](https://jobsthe24.com) their [AI](https://git.gumoio.com) journey and unlock organization worth.<br>
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