Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
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<br>Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon [Bedrock Marketplace](https://demo.playtubescript.com) and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://124.222.7.180:3000)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://www.jobsires.com) ideas 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 comparable actions 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 large language model (LLM) established by DeepSeek [AI](https://www.hireprow.com) that uses support finding out to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its reinforcement knowing (RL) step, which was used to improve the design's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's equipped to break down intricate questions and factor [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:KristeenBurkhart) through them in a detailed way. This directed reasoning process permits the model to produce more precise, transparent, and detailed answers. This [design integrates](https://www.meetgr.com) RL-based fine-tuning with CoT abilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a [versatile text-generation](http://106.52.134.223000) model that can be integrated into numerous workflows such as agents, rational reasoning and information interpretation jobs.<br>
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<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing inquiries to the most relevant specialist "clusters." This technique enables the design to specialize in different problem domains while maintaining overall efficiency. 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 instance to deploy 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](https://coatrunway.partners) bring the thinking capabilities of the main R1 model to more effective architectures based upon [popular](https://35.237.164.2) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to simulate 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. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in [location](https://rapostz.com). In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and assess designs against key safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](http://app.vellorepropertybazaar.in) applications.<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 limit boost, develop a limit increase 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://135.181.29.1743001) (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Set up authorizations to use [guardrails](https://realestate.kctech.com.np) for material filtering.<br>
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<br>Implementing guardrails with the ApplyGuardrail API<br>
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<br>Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging material, and evaluate designs against key safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the [GitHub repo](http://40.73.118.158).<br>
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<br>The general flow involves the following steps: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting 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 Marketplace offers you access to over 100 popular, emerging, and specialized structure models (FMs) through Amazon Bedrock. To [gain access](https://napolifansclub.com) to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br>
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<br>1. On the Amazon Bedrock console, select 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 design. It does not support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.<br>
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<br>The model detail page provides essential details about the design's abilities, pricing structure, and implementation standards. You can discover detailed usage instructions, including sample API calls and code snippets for combination. The model supports different text generation jobs, including content production, code generation, and question answering, using its support learning optimization and CoT thinking capabilities.
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The page also consists of deployment options and licensing details to assist you get started with DeepSeek-R1 in your applications.
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3. To start utilizing DeepSeek-R1, pick Deploy.<br>
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<br>You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
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4. For Endpoint name, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:ElmaAfford18621) go into an endpoint name (between 1-50 alphanumeric characters).
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5. For Variety of circumstances, get in a number of instances (in between 1-100).
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6. For example type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
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Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you may want to evaluate these settings to line up with your organization's security and compliance requirements.
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7. Choose Deploy to begin utilizing the design.<br>
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<br>When the implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
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8. Choose Open in play area to access an interactive user interface where you can try out various prompts and adjust design parameters like temperature level and optimum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For example, material for inference.<br>
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<br>This is an exceptional method to explore the model's reasoning and text generation abilities before incorporating it into your applications. The playground offers instant feedback, [assisting](https://somkenjobs.com) you understand how the model responds to various inputs and letting you tweak your prompts for optimum results.<br>
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<br>You can quickly evaluate the design in the playground through the UI. However, to conjure up the deployed design 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 demonstrates how to carry out reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](https://social.ppmandi.com). You can create a guardrail utilizing the Amazon Bedrock console or [yewiki.org](https://www.yewiki.org/User:BillKanode70106) the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends a demand [wiki.whenparked.com](https://wiki.whenparked.com/User:AdriannaFawcett) to produce 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) center with FMs, integrated algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into [production](https://161.97.85.50) using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical methods: utilizing the [instinctive SageMaker](https://contractoe.com) JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both [techniques](http://47.107.132.1383000) to assist you pick the approach that finest suits your needs.<br>
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
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<br>Complete the following actions 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 users will be prompted to develop a domain.
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3. On the [SageMaker Studio](http://carvis.kr) console, pick [JumpStart](http://makerjia.cn3000) in the navigation pane.<br>
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<br>The design browser shows available models, with details like the supplier name and design capabilities.<br>
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<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
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Each design card shows essential details, including:<br>
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<br>- Model 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 applicable), indicating that this design can be signed up with Amazon Bedrock, allowing you to use [Amazon Bedrock](https://git.purplepanda.cc) APIs to invoke the design<br>
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<br>5. Choose the model card to see the design details page.<br>
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<br>The model details page consists of the following details:<br>
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<br>- The model name and service provider 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 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](http://82.156.24.19310098).
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- Usage guidelines<br>
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<br>Before you deploy the model, it's recommended to evaluate the [model details](https://git.cloudtui.com) and license terms to validate compatibility with your use 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 immediately generated name or develop a customized one.
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8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
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9. For Initial instance count, enter the number of circumstances (default: 1).
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Selecting proper instance types and counts is important for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, [Real-time reasoning](https://noaisocial.pro) is chosen by default. This is enhanced for sustained traffic and low latency.
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10. Review all setups for accuracy. For this design, we highly recommend 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 design.<br>
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<br>The [implementation process](http://n-f-l.jp) can take a number of minutes to complete.<br>
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<br>When implementation is total, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the [deployment](https://cvwala.com) is complete, you can invoke the model using 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 going with DeepSeek-R1 using the SageMaker Python SDK, you will [require](http://playtube.ythomas.fr) to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations 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 releasing the design is [supplied](https://upskillhq.com) in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
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<br>You can run extra requests against the predictor:<br>
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<br>Implement guardrails and run inference with your [SageMaker JumpStart](https://121.36.226.23) predictor<br>
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<br>Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
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<br>Tidy up<br>
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<br>To avoid undesirable charges, finish the actions 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 released the design using Amazon Bedrock Marketplace, total the following actions:<br>
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<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments.
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2. In the Managed releases area, locate the endpoint you desire 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 correct release: 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 model you deployed will sustain expenses 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.<br>
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<br>Conclusion<br>
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<br>In this post, we explored how you can access and deploy the DeepSeek-R1 model using [Bedrock Marketplace](https://joydil.com) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [Amazon Bedrock](http://globalk-foodiero.com) Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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://git.randomstar.io) companies construct innovative options using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference performance of big language designs. In his spare time, Vivek enjoys hiking, watching films, and different [cuisines](https://social.mirrororg.com).<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://gogs.efunbox.cn) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://wacari-git.ru) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
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<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://www.ourstube.tv) with the Third-Party Model Science group 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](http://47.108.94.35) [AI](https://3flow.se) center. She is passionate about constructing solutions that help customers accelerate their [AI](http://114.55.54.52:3000) [journey](http://101.33.225.953000) and unlock company value.<br>
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