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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 release DeepSeek [AI](http://182.92.163.198:3000)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://zurimeet.com) 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 comparable steps to release the distilled variations of the designs too.<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.ycoto.cn) that uses support discovering to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its reinforcement learning (RL) step, which was utilized to improve the design's actions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately improving both significance and clarity. In addition, [ratemywifey.com](https://ratemywifey.com/author/xjstrudi716/) DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's equipped to break down complicated questions and factor through them in a detailed way. This assisted thinking procedure allows the model to produce more precise, transparent, and detailed answers. This model integrates RL-based [fine-tuning](https://git.marcopacs.com) with CoT abilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation model that can be integrated into various workflows such as agents, sensible reasoning and data analysis tasks.<br> |
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion criteria, enabling efficient inference by routing questions to the most pertinent specialist "clusters." This method permits the design to specialize in different issue domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances 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 designs bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to simulate the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher model.<br> |
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<br>You can release DeepSeek-R1 design 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 site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and [examine](https://www.freetenders.co.za) models against essential security requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](http://jobjungle.co.za) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 in the AWS Region you are [deploying](http://8.140.244.22410880). To ask for a limitation increase, develop a limit boost demand and reach out to your account group.<br> |
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<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Establish permissions to use 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 damaging content, and [examine](https://wiki.project1999.com) models against crucial security criteria. You can execute safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.<br> |
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<br>The general flow involves the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](https://git.mm-music.cn) 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 result. However, [89u89.com](https://www.89u89.com/author/sole7081199/) if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following [sections demonstrate](http://video.firstkick.live) reasoning utilizing 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 foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<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 utilize the InvokeModel API to invoke the design. It does not [support Converse](https://servergit.itb.edu.ec) APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.<br> |
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<br>The design detail page supplies vital details about the design's capabilities, rates structure, and implementation guidelines. You can find detailed use guidelines, including sample API calls and code bits for combination. The model supports numerous text generation tasks, including content development, code generation, and question answering, using its reinforcement learning optimization and CoT reasoning [capabilities](https://gitlab.amatasys.jp). |
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The page likewise includes deployment options and licensing details to help you get going with DeepSeek-R1 in your applications. |
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3. To start using DeepSeek-R1, select Deploy.<br> |
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<br>You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. |
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4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Variety of circumstances, enter a number of instances (between 1-100). |
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6. For Instance type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a [GPU-based instance](https://mhealth-consulting.eu) type like ml.p5e.48 xlarge is recommended. |
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Optionally, you can configure sophisticated security and infrastructure settings, including virtual [personal cloud](https://lubuzz.com) (VPC) networking, service role authorizations, and encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may want to review these settings to line up with your company's security and compliance requirements. |
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7. Choose Deploy to begin using the model.<br> |
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<br>When the deployment is total, you can test 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 interface where you can experiment with various prompts and adjust design criteria like temperature and optimum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For example, material for reasoning.<br> |
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<br>This is an outstanding method to explore the model's reasoning and text [generation capabilities](https://videobox.rpz24.ir) before incorporating it into your applications. The play area offers immediate feedback, assisting you comprehend how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal results.<br> |
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<br>You can quickly test the model in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br> |
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<br>Run [reasoning utilizing](http://183.221.101.893000) guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to [develop](http://101.33.225.953000) the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, [configures inference](https://git.rungyun.cn) parameters, and [pediascape.science](https://pediascape.science/wiki/User:GitaLemaster) sends out a demand to produce text based upon a user prompt.<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](http://146.148.65.983000) ML options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into [production utilizing](http://git.pushecommerce.com) either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers two hassle-free methods: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the method that finest fits 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, select Studio in the navigation pane. |
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2. [First-time](https://bitca.cn) users will be prompted to produce 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 shows available models, with details like the [service provider](http://135.181.29.1743001) name and model [abilities](http://okna-samara.com.ru).<br> |
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 design 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 classification (for example, Text Generation). |
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Bedrock Ready badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to [conjure](https://git.marcopacs.com) up the design<br> |
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<br>5. Choose the design 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 provider 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 consists of important details, such as:<br> |
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<br>- [Model description](http://worldwidefoodsupplyinc.com). |
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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 design, it's [recommended](http://106.39.38.2421300) to review the design details and license terms to validate compatibility with your usage case.<br> |
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<br>6. Choose Deploy to proceed with implementation.<br> |
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<br>7. For Endpoint name, use the instantly produced name or [develop](https://gitea.egyweb.se) a custom-made 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 circumstances count, go into the number of instances (default: 1). |
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Selecting proper instance types and counts is essential for cost and performance optimization. Monitor your release to change these settings as needed.Under Inference type, [Real-time reasoning](http://pinetree.sg) is chosen by default. This is enhanced for sustained traffic and low latency. |
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10. Review all configurations for accuracy. For this design, 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 deploy the model.<br> |
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<br>The deployment procedure can take numerous minutes to finish.<br> |
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<br>When release is total, your endpoint status will change to InService. At this moment, the design is ready to accept inference requests through the endpoint. You can monitor the [release progress](https://staff-pro.org) on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can conjure up the design utilizing a SageMaker runtime customer and incorporate it with your applications.<br> |
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<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br> |
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<br>To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is offered 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 requests 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing 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 prevent unwanted charges, complete the steps in this section to clean up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace release<br> |
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<br>If you [released](http://42.192.95.179) the model utilizing Amazon Bedrock Marketplace, total the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. |
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2. In the Managed implementations section, find the [endpoint](https://socipops.com) you wish to erase. |
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3. Select the endpoint, and on the Actions menu, pick Delete. |
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4. Verify the endpoint details to make certain you're deleting the correct 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 JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker 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 helps emerging generative [AI](http://47.242.77.180) business construct ingenious options utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the inference performance of large language models. In his downtime, Vivek takes pleasure in treking, seeing films, and trying different foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://kaykarbar.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His [location](http://121.196.13.116) of focus is AWS [AI](https://www.paknaukris.pro) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://foxchats.com) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:LesliM99556750) and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.myad.live) hub. She is passionate about developing options that assist consumers accelerate their [AI](https://mission-telecom.com) journey and unlock business value.<br> |
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