Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart'
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<br>Today, we are thrilled to reveal that [DeepSeek](http://candidacy.com.ng) R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://gitea.createk.pe)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://gitlab.optitable.com) concepts on AWS.<br> |
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<br>In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar 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 design (LLM) developed by DeepSeek [AI](https://dreamtvhd.com) that utilizes reinforcement discovering to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its reinforcement knowing (RL) step, which was used to refine the model's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's equipped to break down [intricate inquiries](https://www.longisland.com) and reason through them in a detailed manner. This [assisted thinking](http://jatushome.myqnapcloud.com8090) procedure enables the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a [versatile text-generation](https://myvip.at) design that can be incorporated into various workflows such as agents, sensible reasoning and data interpretation jobs.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, making it possible for efficient inference by routing queries to the most appropriate expert "clusters." This method enables the model to specialize in different problem domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 [xlarge circumstances](http://31.184.254.1768078) to deploy the design. 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 reasoning abilities 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 to a procedure of training smaller sized, more effective models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, 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](http://104.248.138.208). Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and assess models against key security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and [Bedrock](http://8.137.89.263000) Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create [numerous](https://iamtube.jp) guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](http://demo.qkseo.in) applications.<br> |
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<br>Prerequisites<br> |
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<br>To deploy 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](http://dev.onstyler.net30300) and under AWS Services, choose Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the [AWS Region](https://macphersonwiki.mywikis.wiki) you are deploying. To request a limit boost, develop a limit boost demand and connect to your account team.<br> |
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<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use 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, prevent harmful content, and examine designs against key safety requirements. You can implement safety procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model reactions released 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 includes the following steps: First, the system gets an input for the design. This input is then [processed](https://jobs.ondispatch.com) 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 applied. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened 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](http://chillibell.com) showcased in the following areas 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 Marketplace](https://www.selfhackathon.com) offers you access to over 100 popular, emerging, and specialized structure models (FMs) through [Amazon Bedrock](https://bcde.ru). 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, choose 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 conjure up 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 pick the DeepSeek-R1 design.<br> |
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<br>The model detail page supplies necessary details about the design's capabilities, pricing structure, and application guidelines. You can discover detailed usage instructions, including sample API calls and code bits for combination. The model supports numerous text generation tasks, consisting of material production, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities. |
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The page likewise consists of implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications. |
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3. To begin 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 Number of circumstances, go into a number of instances (in between 1-100). |
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6. For Instance type, select your instance 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 configure advanced security and infrastructure settings, including virtual [private](https://just-entry.com) cloud (VPC) networking, service function approvals, and encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to align with your organization's security and compliance requirements. |
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7. Choose Deploy to start utilizing the design.<br> |
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<br>When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. |
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8. Choose Open in playground to access an interactive user interface where you can explore different prompts and adjust model specifications like temperature level and maximum length. |
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When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For example, material for reasoning.<br> |
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<br>This is an outstanding method to explore the design's thinking and text generation [abilities](https://git.electrosoft.hr) before integrating it into your applications. The playground supplies immediate feedback, assisting you understand how the model reacts to different inputs and letting you tweak your prompts for optimum results.<br> |
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<br>You can rapidly 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 inference using guardrails with the deployed DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends out a demand to [produce text](http://1.94.27.2333000) 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, integrated 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 data, and release them into production utilizing either the UI or SDK.<br> |
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<br>[Deploying](https://edge1.co.kr) DeepSeek-R1 model through SageMaker JumpStart offers 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the approach that finest matches your needs.<br> |
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<br>Deploy DeepSeek-R1 through [SageMaker JumpStart](https://manchesterunitedfansclub.com) 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](http://40.73.118.158) users will be triggered to develop a domain. |
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3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br> |
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<br>The model browser displays available designs, with details like the supplier name and design capabilities.<br> |
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. |
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Each design card shows key details, consisting of:<br> |
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<br>[- Model](https://git.yqfqzmy.monster) name |
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- Provider name |
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- Task category (for instance, Text Generation). |
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Bedrock Ready badge (if appropriate), indicating that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design<br> |
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<br>5. Choose the model 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 company details. |
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Deploy button to [release](https://www.askmeclassifieds.com) the design. |
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About and Notebooks tabs with detailed details<br> |
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<br>The About tab includes important details, such as:<br> |
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<br>- Model description. |
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- License details. |
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- Technical [specifications](http://47.97.159.1443000). |
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- Usage standards<br> |
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<br>Before you release the model, it's recommended to evaluate the model details and license terms to verify compatibility with your use case.<br> |
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<br>6. Choose Deploy to continue with release.<br> |
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<br>7. For Endpoint name, use the immediately generated name or create 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 instance count, enter the variety of circumstances (default: 1). |
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Selecting proper instance types and counts is vital for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by [default](https://wiki.dulovic.tech). This is optimized for sustained traffic and low latency. |
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10. Review all configurations for accuracy. For this model, we strongly recommend 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 design.<br> |
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<br>The release procedure can take a number of minutes to complete.<br> |
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<br>When release is total, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning requests through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will show pertinent metrics and . When the release is complete, you can conjure up the design 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 utilizing the SageMaker Python SDK, you will [require](https://neejobs.com) to set up the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for [deploying](https://thaisfriendly.com) the design is supplied 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 reasoning with your SageMaker JumpStart predictor<br> |
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<br>Similar to Amazon Bedrock, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1090221) you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:<br> |
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<br>Clean up<br> |
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<br>To prevent undesirable charges, complete the steps in this area 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 released the design using [Amazon Bedrock](http://git.attnserver.com) Marketplace, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace implementations. |
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2. In the Managed implementations section, locate the endpoint you want 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 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](https://scienetic.de) 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 [checked](https://git.mhurliman.net) 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 start. For more details, describe Use [Amazon Bedrock](http://123.60.173.133000) tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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://www.paradigmrecruitment.ca) companies construct innovative services utilizing AWS services and accelerated compute. Currently, he is focused on establishing methods for fine-tuning and optimizing the reasoning efficiency of big language designs. In his free time, Vivek enjoys hiking, seeing motion pictures, and trying various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://xn--vk1b975azoatf94e.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://socipops.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br> |
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<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://music.afrixis.com) with the Third-Party Model Science team at AWS.<br> |
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<br>Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.sociopost.co.uk) hub. She is enthusiastic about constructing solutions that assist consumers accelerate their [AI](http://ecoreal.kr) journey and [unlock service](http://47.100.220.9210001) worth.<br> |
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