Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are thrilled to announce that DeepSeek R1 distilled Llama and [Qwen designs](https://uedf.org) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://codeh.genyon.cn)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your [generative](https://eastcoastaudios.in) [AI](https://gurjar.app) 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 versions of the models also.<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](http://rapz.ru) that uses support discovering to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating feature is its reinforcement knowing (RL) action, which was used to fine-tune the design's actions beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately enhancing both [relevance](https://gitea.easio-com.com) and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's geared up to break down complex questions and factor through them in a detailed way. This directed reasoning procedure allows the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, sensible reasoning and data interpretation 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 allows activation of 37 billion parameters, allowing effective reasoning by routing queries to the most appropriate professional "clusters." This method permits the model to focus on different problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to [release](https://hub.bdsg.academy) the model. ml.p5e.48 xlarge includes 8 Nvidia H200 [GPUs supplying](http://106.55.234.1783000) 1128 GB of GPU memory.<br> |
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<br>DeepSeek-R1 distilled models bring the [thinking capabilities](http://xn--mf0bm6uh9iu3avi400g.kr) of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). [Distillation refers](https://git.declic3000.com) to a procedure of training smaller sized, more efficient models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing 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 model, we recommend deploying this model with guardrails in place. In this blog, we will utilize Amazon Bedrock [Guardrails](https://lovetechconsulting.net) to introduce safeguards, prevent harmful material, and evaluate designs against essential safety criteria. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://lethbridgegirlsrockcamp.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 check if you have quotas for P5e, open the Service Quotas [console](https://www.opad.biz) 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 deploying. To request a limitation boost, create a limitation boost request and connect to your account group.<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) authorizations 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](http://git.acdts.top3000) API<br> |
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<br>Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful content, and examine models against key security criteria. You can execute safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a [guardrail utilizing](https://tangguifang.dreamhosters.com) the Amazon Bedrock [console](https://groups.chat) or the API. For the example code to develop 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 the guardrail check, it's sent to the design for inference. After receiving the model'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 showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate 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](https://testgitea.educoder.net) Marketplace offers you access to over 100 popular, emerging, and specialized structure 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, choose Model brochure under Foundation designs in the navigation pane. |
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At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.<br> |
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<br>The design detail page offers necessary details about the model's abilities, pricing structure, and application guidelines. You can find detailed usage guidelines, consisting of sample API calls and code snippets for integration. The design supports various text generation jobs, including material development, code generation, and concern answering, using its support discovering optimization and CoT reasoning abilities. |
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The page also includes release options and licensing [details](https://skillsvault.co.za) to help you begin with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, select Deploy.<br> |
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<br>You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated. |
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4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). |
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5. For Variety of circumstances, go into a variety of instances (between 1-100). |
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6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may want to examine these settings to line up with your company's security and compliance requirements. |
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7. Choose Deploy to start [utilizing](http://dkjournal.co.kr) the model.<br> |
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<br>When the implementation is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. |
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8. Choose Open in play area to access an interactive user interface where you can try out various triggers and adjust design parameters like temperature level 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 results. For example, content for inference.<br> |
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<br>This is an exceptional method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play ground supplies instant feedback, helping you understand how the design reacts to various inputs and letting you fine-tune your prompts for [optimal outcomes](http://git.ndjsxh.cn10080).<br> |
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<br>You can quickly check the design in the play ground through the UI. However, to invoke the deployed design 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 deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 design 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 developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_[runtime](http://223.68.171.1508004) client, sets up [reasoning](https://social.sktorrent.eu) specifications, and sends a demand to create 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, integrated algorithms, and prebuilt ML solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and release them into production using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical techniques: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the approach that finest matches 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 release DeepSeek-R1 using SageMaker JumpStart:<br> |
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<br>1. On the SageMaker console, [choose Studio](http://kuzeydogu.ogo.org.tr) in the navigation pane. |
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2. First-time users will be triggered to develop a domain. |
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3. On the SageMaker Studio console, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) choose JumpStart in the navigation pane.<br> |
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<br>The model web browser displays available designs, with details like the supplier name and model abilities.<br> |
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<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. |
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Each design card shows 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 example, Text Generation). |
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Bedrock Ready badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, permitting you to use [Amazon Bedrock](https://www.jobmarket.ae) APIs to conjure up the model<br> |
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<br>5. Choose the model card to view the design details page.<br> |
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<br>The design details page includes 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 important 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 deploy the design, 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 [deployment](https://vacancies.co.zm).<br> |
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<br>7. For Endpoint name, utilize the automatically created name or create a customized one. |
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8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge). |
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9. For Initial circumstances count, go into the variety of instances (default: 1). |
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Selecting proper instance types and counts is important for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is chosen 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 highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. |
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11. Choose Deploy to deploy the design.<br> |
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<br>The release process can take numerous minutes to finish.<br> |
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<br>When deployment is complete, your endpoint status will change to InService. At this moment, the model is prepared to accept reasoning requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show [relevant metrics](https://addismarket.net) and status details. When the implementation is complete, you can invoke the model utilizing 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 begin with DeepSeek-R1 utilizing 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 shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:<br> |
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<br>Tidy up<br> |
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<br>To avoid undesirable charges, complete the actions in this area to clean up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace deployment<br> |
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<br>If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following actions:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. |
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2. In the Managed implementations 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 deleting the appropriate 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](https://mypetdoll.co.kr) you deployed will sustain costs 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://xajhuang.com3100) or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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 assists emerging generative [AI](http://git.iloomo.com) companies develop ingenious services utilizing AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of large language designs. In his complimentary time, Vivek takes pleasure in hiking, seeing motion pictures, and trying various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://mmatycoon.info) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://www.oscommerce.com) 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 dealing with generative [AI](https://dimension-gaming.nl) with the Third-Party Model Science group at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://media.clear2work.com.au) hub. She is [passionate](http://101.132.163.1963000) about [constructing options](http://118.25.96.1183000) that help consumers accelerate their [AI](https://actsfile.com) journey and unlock company value.<br> |
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