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<br>Today, we are thrilled 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](http://b-ways.sakura.ne.jp)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](http://www.cl1024.online) concepts on AWS.<br> |
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<br>In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the models as well.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://git.yingcaibx.com) that uses support discovering to [improve thinking](https://gitlab.dituhui.com) capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its [support knowing](https://radicaltarot.com) (RL) action, which was used to fine-tune the model's actions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's equipped to break down intricate inquiries and factor through them in a detailed way. This guided reasoning process allows the model to [produce](http://1.92.66.293000) more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation model that can be incorporated into various workflows such as representatives, sensible reasoning and data interpretation jobs.<br> |
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, allowing effective inference by routing inquiries to the most appropriate expert "clusters." This method enables the design to concentrate on different problem domains while maintaining general [performance](https://jobz1.live). DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 [xlarge instance](https://www.globaltubedaddy.com) to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the [thinking abilities](http://www5a.biglobe.ne.jp) of the main R1 design to more [efficient architectures](https://www.blatech.co.uk) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher model.<br> |
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<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in location. In this blog, we will utilize Amazon Bedrock [Guardrails](https://great-worker.com) to introduce safeguards, avoid hazardous material, and evaluate models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and [Bedrock](http://app.ruixinnj.com) Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](http://112.74.93.66:22234) applications.<br> |
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<br>Prerequisites<br> |
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<br>To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit increase, develop a limit 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 appropriate AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up permissions 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 enables you to present safeguards, avoid harmful content, and examine designs against crucial security criteria. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock [ApplyGuardrail](https://www.passadforbundet.se) API. This enables you to use guardrails to evaluate user inputs and model responses 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 develop the guardrail, see the GitHub repo.<br> |
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<br>The basic flow involves the following steps: First, the system gets an input for the model. 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](https://chaakri.com) is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas show [reasoning utilizing](https://meephoo.com) this API.<br> |
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<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br> |
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<br>Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, pick 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 conjure up the design. It doesn't support Converse 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, prices structure, and execution standards. You can discover detailed usage directions, including sample API calls and code bits for combination. The design supports different text generation tasks, consisting of content production, code generation, and question answering, using its support discovering optimization and CoT thinking abilities. |
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The page likewise includes implementation options and licensing details to assist you get begun with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, choose Deploy.<br> |
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<br>You will be triggered to set up the release details for DeepSeek-R1. The model 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 Variety of instances, enter a number of circumstances (in between 1-100). |
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6. For example type, select your instance type. For optimal efficiency with DeepSeek-R1, a [GPU-based](https://drshirvany.ir) instance type like ml.p5e.48 xlarge is recommended. |
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Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you may desire to evaluate these settings to line up with your organization's security and compliance requirements. |
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7. Choose Deploy to begin utilizing the design.<br> |
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<br>When the release is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. |
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8. Choose Open in play ground to access an interactive user interface where you can explore various prompts and change model specifications like temperature and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For instance, content for inference.<br> |
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<br>This is an outstanding method to check out the model's thinking and text generation capabilities before integrating it into your applications. The playground offers immediate feedback, helping you comprehend how the design reacts to various inputs and letting you fine-tune your triggers for optimum results.<br> |
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<br>You can rapidly test the model in the play area through the UI. However, to conjure up 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 deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, [utilize](https://code.linkown.com) the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends a request to produce 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) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://memorial-genweb.org) models to your usage case, with your information, and release them into [production](https://mhealth-consulting.eu) using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient approaches: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the technique that finest suits your [requirements](https://www.flytteogfragttilbud.dk).<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, [choose Studio](https://topcareerscaribbean.com) 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, select JumpStart in the navigation pane.<br> |
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<br>The model browser shows available models, with details like the provider name and design abilities.<br> |
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<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. |
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Each model card reveals key details, including:<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 relevant), suggesting that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design<br> |
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<br>5. Choose the [design card](https://elitevacancies.co.za) to see the design details page.<br> |
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<br>The [model details](https://slovenskymedved.sk) page includes the following details:<br> |
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<br>- The model name and provider details. |
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Deploy button to release the model. |
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About and Notebooks tabs with detailed details<br> |
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<br>The About tab consists of important details, such as:<br> |
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<br>- [Model description](http://104.248.138.208). |
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- License details. |
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- Technical requirements. |
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- Usage guidelines<br> |
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<br>Before you deploy the model, it's advised to evaluate the design details and license terms to confirm compatibility with your usage case.<br> |
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<br>6. Choose Deploy to continue with implementation.<br> |
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<br>7. For Endpoint name, use the automatically generated name or develop a custom one. |
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8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial instance count, go into the variety of circumstances (default: 1). |
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Selecting appropriate instance types and counts is important for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. |
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10. Review all configurations for precision. For this model, we highly suggest sticking 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 implementation process can take several minutes to complete.<br> |
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<br>When deployment is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning requests through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can [conjure](https://adsall.net) up the model using a SageMaker runtime customer and incorporate it with your [applications](https://www.bongmedia.tv).<br> |
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<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br> |
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<br>To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the notebook 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 inference 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](https://trulymet.com) predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:<br> |
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<br>Tidy up<br> |
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<br>To avoid [unwanted](https://trackrecord.id) charges, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:AthenaLucas) finish the actions in this area 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 model utilizing 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, choose Marketplace implementations. |
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2. In the Managed releases area, locate the endpoint you wish to erase. |
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3. Select the endpoint, and on the Actions menu, choose Delete. |
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4. Verify the endpoint details to make certain you're deleting the right release: 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 JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to erase 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 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 JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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://mixup.wiki) companies develop ingenious options using AWS services and sped up compute. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the inference performance of large language designs. In his leisure time, Vivek enjoys hiking, viewing films, and attempting different foods.<br> |
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<br>[Niithiyn Vijeaswaran](https://gitea.alexconnect.keenetic.link) is a Generative [AI](http://121.28.134.38:2039) Specialist Solutions Architect with the Third-Party Model [Science](https://wiki.monnaie-libre.fr) team at AWS. His of focus is AWS [AI](https://wiki.contextgarden.net) [accelerators](https://cvwala.com) (AWS Neuron). He holds a Bachelor's degree in Computer Science and [disgaeawiki.info](https://disgaeawiki.info/index.php/User:Mari220954) Bioinformatics.<br> |
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<br>[Jonathan Evans](https://gitea.linkensphere.com) is a Specialist Solutions Architect dealing with generative [AI](http://129.151.171.122:3000) with the [Third-Party Model](https://www.jobassembly.com) Science team at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://idaivelai.com) hub. She is passionate about building services that assist clients accelerate their [AI](https://www.xtrareal.tv) journey and unlock service value.<br> |
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