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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://wisewayrecruitment.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://www.rybalka.md) ideas 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 actions 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 large language model (LLM) established by DeepSeek [AI](https://git.rggn.org) that uses reinforcement finding out to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its reinforcement learning (RL) action, which was utilized to refine the design's reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, implying it's geared up to break down complicated questions and factor through them in a detailed manner. This assisted reasoning procedure enables the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be integrated into various workflows such as agents, logical thinking and data interpretation tasks.<br>
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<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, allowing efficient inference by routing queries to the most appropriate professional "clusters." This method permits the design to focus on different problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 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 includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based upon 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 models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, using 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 design, we suggest releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and examine designs against key security criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](https://www.cupidhive.com) 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 circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 instance in the AWS Region you are releasing. To request a limitation boost, create a limitation boost request and reach out to your account group.<br>
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<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To [Management](http://120.55.164.2343000) (IAM) [consents](http://suvenir51.ru) to use Amazon Bedrock Guardrails. For directions, see Set up consents to utilize 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 allows you to present safeguards, prevent harmful material, and evaluate designs against key security requirements. You can execute security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://hafrikplay.com). You can produce a [guardrail utilizing](https://siman.co.il) 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 general flow includes the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the [input passes](https://codeh.genyon.cn) the guardrail check, it's sent out to the design for [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:NestorAldrich) inference. 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, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections [demonstrate reasoning](https://www.jaitun.com) 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 gives you access to over 100 popular, emerging, and [specialized foundation](http://218.28.28.18617423) designs (FMs) through [Amazon Bedrock](https://vooxvideo.com). 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, 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 does not support Converse APIs and other [Amazon Bedrock](http://git.andyshi.cloud) tooling.
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2. Filter for DeepSeek as a [supplier](http://images.gillion.com.cn) and pick the DeepSeek-R1 design.<br>
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<br>The model detail page [supplies](http://aiot7.com3000) important details about the model's capabilities, pricing structure, and application guidelines. You can discover detailed usage instructions, including sample API calls and code snippets for integration. The design supports numerous text generation tasks, consisting of content production, code generation, and question answering, using its reinforcement learning optimization and CoT thinking abilities.
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The page likewise consists of deployment alternatives and licensing details to help you start with DeepSeek-R1 in your [applications](https://admithel.com).
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3. To begin using DeepSeek-R1, pick Deploy.<br>
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<br>You will be prompted to [configure](http://hmkjgit.huamar.com) the implementation details for DeepSeek-R1. The design ID will be pre-populated.
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4. For [Endpoint](http://1.92.66.293000) name, enter an endpoint name (between 1-50 alphanumeric characters).
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5. For [Variety](https://wiki.vifm.info) of circumstances, get in a variety of instances (between 1-100).
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6. For Instance type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
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Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to line up with your company's security and .
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7. Choose Deploy to start using the design.<br>
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<br>When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
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8. Choose Open in playground to access an interactive interface where you can explore different triggers and change design parameters like temperature level and optimum length.
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For instance, material for reasoning.<br>
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<br>This is an exceptional method to check out the design's thinking and text generation capabilities before integrating it into your applications. The play ground provides instant feedback, helping you comprehend how the design reacts to various inputs and [letting](http://deve.work3000) you fine-tune your prompts for ideal results.<br>
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<br>You can quickly check 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 reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to perform reasoning utilizing a released 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 produce 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 a request to generate text based on 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) center with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into [production utilizing](https://followmypic.com) either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 [hassle-free](https://job-daddy.com) techniques: using the intuitive SageMaker JumpStart UI or implementing [programmatically](https://git.nazev.eu) through the SageMaker Python SDK. Let's check out both approaches to assist you choose the technique that best 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 using 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](https://www.chinami.com) users will be triggered 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 internet [browser displays](https://www.buzzgate.net) available models, with [details](http://code.exploring.cn) like the provider name and design capabilities.<br>
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
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Each model card reveals key 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 relevant), suggesting that this model can be signed up with Amazon Bedrock, enabling 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 model details page consists of the following details:<br>
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<br>- The design name and service provider details.
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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 includes essential details, such as:<br>
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<br>- Model description.
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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 release the model, it's recommended to examine the model details and license terms to verify 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](https://local.wuanwanghao.top3000) name, use the automatically produced name or produce 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 circumstances count, go into the number of [circumstances](http://www.umzumz.com) (default: 1).
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Selecting suitable circumstances types and counts is vital for expense and [efficiency optimization](https://mtglobalsolutionsinc.com). Monitor your release to change these settings as needed.Under Inference type, [Real-time inference](http://60.204.229.15120080) is chosen by default. This is enhanced for sustained traffic and [low latency](https://jobstaffs.com).
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10. Review all setups for [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/3099171) precision. For this model, we highly advise [sticking](https://talentocentroamerica.com) to SageMaker JumpStart default settings and making certain that network isolation remains in place.
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11. Choose Deploy to release the design.<br>
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<br>The deployment process can take several minutes to complete.<br>
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<br>When deployment is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can invoke the model utilizing a SageMaker runtime client 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 set up the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the note pad and range 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 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. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:<br>
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<br>Clean up<br>
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<br>To avoid undesirable charges, finish the actions in this section to tidy up your [resources](http://106.52.126.963000).<br>
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<br>Delete the Amazon Bedrock Marketplace deployment<br>
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<br>If you deployed the design utilizing Amazon Bedrock Marketplace, complete 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 deployments area, locate the endpoint you desire to delete.
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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 right 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 costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and [Resources](http://szyg.work3000).<br>
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<br>Conclusion<br>
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<br>In this post, we checked out how you can access and deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use [Amazon Bedrock](http://121.42.8.15713000) tooling with Amazon SageMaker JumpStart designs, [SageMaker JumpStart](https://wiki.rrtn.org) [pretrained](https://git.jerl.dev) designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker [JumpStart](https://nepalijob.com).<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://git.arcbjorn.com) business construct innovative options using AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the inference performance of big language designs. In his totally free time, Vivek takes pleasure in treking, enjoying motion pictures, and trying different cuisines.<br>
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<br>[Niithiyn Vijeaswaran](https://app.joy-match.com) is a Generative [AI](http://dasaram.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://estekhdam.in) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://gitlab.hanhezy.com) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://akinsemployment.ca) hub. She is enthusiastic about [building options](https://git.owlhosting.cloud) that help customers accelerate their [AI](https://tiptopface.com) journey and unlock service value.<br>
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