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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 release DeepSeek [AI](http://39.101.134.26:9800)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://hlatube.com) concepts on AWS.<br>
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<br>In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://2workinoz.com.au). You can follow similar steps to deploy the [distilled versions](https://careerjunction.org.in) of the designs as well.<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://diskret-mote-nodeland.jimmyb.nl) that uses reinforcement discovering to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial distinguishing function is its support learning (RL) step, which was [utilized](https://git.howdoicomputer.lol) to refine the model's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's geared up to break down complex inquiries and reason through them in a detailed manner. This guided thinking process allows the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, rational thinking and information interpretation tasks.<br>
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<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, enabling effective reasoning by routing inquiries to the most pertinent expert "clusters." This approach permits the design to focus on different issue domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
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<br>DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more effective architectures based on 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 sized, more efficient models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as a [teacher design](http://git.superiot.net).<br>
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<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://hiremegulf.com) model, we suggest deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and evaluate models against key safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and [standardizing security](http://woorichat.com) controls throughout your generative [AI](https://www.elitistpro.com) applications.<br>
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<br>Prerequisites<br>
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<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and [validate](https://shiatube.org) you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limitation increase, develop a limitation boost request and reach out 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 appropriate [AWS Identity](https://gitea.oo.co.rs) and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For instructions, see Set up consents 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 introduce safeguards, avoid harmful content, and examine designs against crucial safety criteria. You can execute security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions deployed 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.<br>
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<br>The basic circulation includes the following steps: 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 getting the model's output, another guardrail check is applied. If the [output passes](https://gitea.createk.pe) this final check, it's [returned](http://xn--ok0b74gbuofpaf7p.com) 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 stage. The examples showcased in the following sections demonstrate reasoning 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://oros-git.regione.puglia.it) to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
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<br>1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane.
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At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
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2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.<br>
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<br>The model detail page provides [essential details](http://gbtk.com) about the design's capabilities, rates structure, and [implementation standards](https://district-jobs.com). You can find detailed usage guidelines, including sample API calls and code bits for integration. The design supports various text generation jobs, including content development, code generation, and concern answering, using its support discovering optimization and CoT thinking abilities.
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The page also includes implementation choices and licensing details to assist you start with DeepSeek-R1 in your applications.
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3. To start using DeepSeek-R1, choose Deploy.<br>
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<br>You will be prompted to set up the implementation 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](https://wiki.lspace.org) characters).
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5. For Number of instances, get in a number of [circumstances](https://ou812chat.com) (between 1-100).
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6. For [Instance](https://just-entry.com) type, select your instance 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 sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service role consents, and file encryption [settings](https://empleos.dilimport.com). For a lot of use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to line up with your company'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 deployment is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
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8. Choose Open in playground to access an interactive interface where you can try out different prompts and change design criteria like temperature and optimum length.
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, material for reasoning.<br>
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<br>This is an exceptional method to check out the model's reasoning and text generation abilities before integrating it into your applications. The play ground offers immediate feedback, assisting you comprehend how the model responds to numerous inputs and letting you tweak your prompts for optimal outcomes.<br>
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<br>You can quickly evaluate the model in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the [endpoint](http://kcinema.co.kr) ARN.<br>
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<br>Run inference using guardrails with the released DeepSeek-R1 endpoint<br>
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<br>The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock [utilizing](http://www.jimtangyh.xyz7002) 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 produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends a request to produce 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, [integrated](https://www.hrdemployment.com) algorithms, and prebuilt ML that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into [production](https://probando.tutvfree.com) using either the UI or SDK.<br>
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides two practical techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the approach that finest 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 release DeepSeek-R1 utilizing SageMaker JumpStart:<br>
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<br>1. On the [SageMaker](http://wiki.iurium.cz) console, select Studio 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, choose JumpStart in the navigation pane.<br>
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<br>The design browser displays available designs, with details like the service provider 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 model card reveals 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 suitable), showing that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon [Bedrock APIs](https://git.hackercan.dev) to invoke the model<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 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 model.
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About and Notebooks tabs with [detailed](https://www.p3r.app) details<br>
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<br>The About tab consists of essential details, such as:<br>
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<br>- Model description.
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- License details.
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- Technical specifications.
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- Usage guidelines<br>
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<br>Before you deploy the model, it's recommended to evaluate the design details and license terms to verify compatibility with your use case.<br>
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<br>6. Choose Deploy to continue with deployment.<br>
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<br>7. For Endpoint name, utilize the instantly generated name or develop a custom 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 [instances](http://115.238.142.15820182) (default: 1).
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Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your implementation to adjust these [settings](http://120.46.139.31) as needed.Under Inference type, Real-time reasoning is picked by default. This is [enhanced](http://42.192.95.179) for sustained traffic and low latency.
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10. Review all configurations for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
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11. Choose Deploy to release the model.<br>
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<br>The [release procedure](https://weeddirectory.com) can take several minutes to complete.<br>
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<br>When deployment is total, your endpoint status will change to InService. At this point, the model is all set to [accept reasoning](https://source.futriix.ru) demands through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can conjure up the design using a SageMaker runtime customer and [integrate](https://humped.life) 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad 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 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 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>Tidy up<br>
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<br>To prevent undesirable charges, finish the steps in this section to tidy 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 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, select Marketplace [releases](https://arlogjobs.org).
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2. In the [Managed implementations](http://ratel.ng) section, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:RileyPaten44059) find the endpoint you wish 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 deleting the proper deployment: 1. Endpoint name.
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2. Model name.
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3. [Endpoint](http://www.hxgc-tech.com3000) status<br>
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<br>Delete the SageMaker JumpStart predictor<br>
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<br>The SageMaker JumpStart model you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio 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 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 assists emerging generative [AI](https://bikapsul.com) companies build innovative services utilizing AWS services and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference efficiency of big language designs. In his leisure time, Vivek delights in hiking, viewing films, and attempting various [cuisines](https://bolsadetrabajo.tresesenta.mx).<br>
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://59.110.162.91:8081) Specialist Solutions Architect with the Third-Party Model [Science team](https://ai.ceo) at AWS. His area of focus is AWS [AI](https://intunz.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 Professional Solutions Architect working on generative [AI](https://tintinger.org) with the Third-Party Model Science group at AWS.<br>
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<br>Banu Nagasundaram leads item, engineering, and [tactical collaborations](https://git.haowumc.com) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.jobzalerts.com) center. She is enthusiastic about building options that help clients accelerate their [AI](http://120.79.211.173:3000) journey and [unlock service](https://aubameyangclub.com) value.<br>
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