Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are delighted to announce that DeepSeek R1 distilled Llama and [Qwen designs](http://famedoot.in) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://foxchats.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://gitea.johannes-hegele.de) ideas on AWS.<br> |
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<br>In this post, we demonstrate how to get started with DeepSeek-R1 on [Amazon Bedrock](https://git.thunraz.se) Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations 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 model (LLM) developed by DeepSeek [AI](https://git.purplepanda.cc) that utilizes reinforcement finding out to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial distinguishing feature is its support learning (RL) step, which was used to fine-tune the model's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's geared up to break down complicated queries and factor through them in a detailed way. This [assisted thinking](http://git.wh-ips.com) process allows the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its [wide-ranging abilities](http://121.37.208.1923000) DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, logical thinking and data analysis tasks.<br> |
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, making it possible for effective reasoning by routing questions to the most pertinent expert "clusters." This technique enables the design to specialize in different problem domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory 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 features 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 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, 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 model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to [introduce](https://www.diltexbrands.com) safeguards, avoid harmful content, and examine designs against key safety requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://bence.net) 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, choose Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. 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, develop a limitation boost request and connect to your account team.<br> |
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<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals 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 allows you to present safeguards, avoid hazardous content, and examine designs against key security requirements. You can implement security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a [guardrail utilizing](http://git.medtap.cn) the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br> |
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<br>The basic circulation includes 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 to the model for reasoning. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. 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 inference 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 provides you access to over 100 popular, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:BarneyAngel760) emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, select [Model brochure](https://ibs3457.com) under Foundation models in the navigation pane. |
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At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.<br> |
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<br>The design detail page supplies vital details about the design's abilities, rates structure, and implementation standards. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The design supports various text generation jobs, [consisting](http://116.63.157.38418) of material creation, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. |
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The page also includes deployment alternatives and licensing details to help you start with DeepSeek-R1 in your [applications](http://git.sdkj001.cn). |
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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 deployment 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 Number of instances, get in a number of circumstances (in between 1-100). |
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6. For Instance type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. |
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Optionally, you can configure sophisticated security and facilities settings, [including virtual](http://gitea.smartscf.cn8000) personal cloud (VPC) networking, service role authorizations, and encryption settings. For the majority of use cases, the [default settings](https://dngeislgeijx.homes) will work well. However, for production releases, you might want to review these settings to align with your company's security and compliance requirements. |
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7. Choose Deploy to [start utilizing](https://gitea.evo-labs.org) the design.<br> |
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<br>When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. |
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8. Choose Open in playground to access an interactive interface where you can experiment with different triggers and adjust model criteria like temperature level and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, content for reasoning.<br> |
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<br>This is an excellent way to check out the model's thinking and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, helping you comprehend how the design responds to numerous inputs and letting you tweak your triggers for optimum results.<br> |
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<br>You can rapidly test the design 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 using guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to carry out inference utilizing a [deployed](http://140.143.226.1) DeepSeek-R1 design 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up [reasoning](http://git.maxdoc.top) parameters, and sends out a demand to create [text based](https://www.wcosmetic.co.kr5012) 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, built-in algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.<br> |
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<br> DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient approaches: utilizing the intuitive SageMaker [JumpStart UI](https://gochacho.com) or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the method that finest fits your requirements.<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 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, pick 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, choose JumpStart in the navigation pane.<br> |
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<br>The model internet browser shows available designs, with details like the service provider name and model abilities.<br> |
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. |
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Each model card reveals crucial details, consisting of:<br> |
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<br>- Model name |
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- Provider name |
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- Task classification (for instance, Text Generation). |
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Bedrock Ready badge (if appropriate), suggesting that this model can be signed up with Amazon Bedrock, [enabling](http://lty.co.kr) you to utilize Amazon Bedrock APIs to conjure up the model<br> |
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<br>5. Choose the design card to see the model details page.<br> |
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<br>The design details page consists of the following details:<br> |
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<br>- The model name and company 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 crucial 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 guidelines<br> |
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<br>Before you release the model, it's recommended to evaluate the design details and license terms to validate 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, [utilize](https://www.jobs.prynext.com) the immediately created name or create a customized one. |
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8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, enter the variety of instances (default: 1). |
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Selecting proper circumstances types and counts is crucial for expense and performance optimization. Monitor your implementation to change these [settings](http://47.75.109.82) 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 accuracy. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. |
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11. Choose Deploy to deploy the design.<br> |
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<br>The release process can take several minutes to complete.<br> |
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<br>When implementation is total, your endpoint status will change to InService. At this point, the design is ready to [accept reasoning](https://evove.io) requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the [design utilizing](https://www.sportfansunite.com) a SageMaker runtime client and integrate 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 get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design 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 requests](https://code.oriolgomez.com) 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 implement it as displayed in the following code:<br> |
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<br>Clean up<br> |
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<br>To prevent undesirable charges, finish the steps 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 design using Amazon Bedrock Marketplace, complete 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 releases section, find the endpoint you desire to delete. |
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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 erasing the proper deployment: 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 released will sustain expenses 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 checked out how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [SageMaker JumpStart](https://somo.global) pretrained designs, 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](https://juryi.sn) business construct ingenious options using AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and enhancing the inference efficiency of large language designs. In his spare time, Vivek enjoys hiking, [viewing](https://wikibase.imfd.cl) films, and trying various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://thevesti.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://theneverendingstory.net) accelerators (AWS Neuron). He holds a [Bachelor's degree](https://bio.rogstecnologia.com.br) in Computer Science and Bioinformatics.<br> |
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://phoebe.roshka.com) with the Third-Party Model [Science team](https://newhopecareservices.com) at AWS.<br> |
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<br>Banu Nagasundaram leads product, engineering, and [tactical partnerships](http://optx.dscloud.me32779) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://connect.taifany.com) hub. She is enthusiastic about building solutions that assist customers accelerate their [AI](http://52.23.128.62:3000) journey and unlock business value.<br> |
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