Update '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 and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://forum.batman.gainedge.org)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your [generative](https://tokemonkey.com) [AI](http://maitri.adaptiveit.net) ideas on AWS.<br> |
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<br>In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can [follow comparable](http://connect.lankung.com) steps to deploy 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 big language design (LLM) established by DeepSeek [AI](https://thaisfriendly.com) that utilizes reinforcement finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing feature is its reinforcement learning (RL) action, which was used to fine-tune the model's actions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it's equipped to break down intricate queries and reason through them in a detailed way. This guided reasoning [process permits](https://links.gtanet.com.br) the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, sensible thinking and data analysis tasks.<br> |
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<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, allowing efficient inference by [routing](https://quikconnect.us) questions to the most relevant specialist "clusters." This approach allows the design to focus on various problem domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. 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 model to more effective 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 sized, more efficient designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.<br> |
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<br>You can [release](https://nytia.org) DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and examine designs against crucial safety [requirements](https://paksarkarijob.com). At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user [experiences](https://pak4job.com) and standardizing security controls across your generative [AI](https://sneakerxp.com) applications.<br> |
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<br>Prerequisites<br> |
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<br>To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify 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 ask for a limitation increase, create a limit increase request and connect to your account group.<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) approvals to use Amazon Bedrock Guardrails. For directions, see Establish approvals 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, avoid harmful content, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:DanaeLegge874) and evaluate designs against essential security criteria. You can execute security steps for the DeepSeek-R1 [model utilizing](https://git.rungyun.cn) the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using 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 basic circulation involves the following steps: First, the system gets 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 design for [inference](http://39.106.177.1608756). After receiving the model's output, another guardrail check is applied. If the [output passes](https://git.thatsverys.us) this final check, it's returned as the outcome. 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 took place at the input or output stage. The examples 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 offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br> |
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<br>1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. |
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At the time of writing this post, you can use the InvokeModel API to conjure up 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 select the DeepSeek-R1 model.<br> |
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<br>The design detail page supplies important details about the model's abilities, rates structure, and implementation guidelines. You can find detailed use guidelines, consisting of sample API calls and code snippets for combination. The design supports different text generation jobs, consisting of material development, code generation, and concern answering, using its reinforcement finding out optimization and CoT reasoning capabilities. |
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The page also consists of release alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. |
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3. To start utilizing DeepSeek-R1, choose Deploy.<br> |
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<br>You will be triggered to [configure](http://47.105.104.2043000) 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 characters). |
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5. For Number of circumstances, enter a variety of circumstances (between 1-100). |
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6. For example type, select your instance type. For optimal 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 innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function consents, and file encryption settings. For a lot of use cases, the default [settings](https://tikness.com) will work well. However, for production releases, you may desire to evaluate these settings to align with your organization's security and compliance requirements. |
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7. Choose Deploy to begin using the model.<br> |
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<br>When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. |
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8. Choose Open in play ground to access an interactive interface where you can try out different prompts and change model parameters like temperature 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 results. For instance, material for reasoning.<br> |
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<br>This is an excellent way to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, helping you comprehend how the design reacts to various inputs and letting you tweak your prompts for optimum outcomes.<br> |
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<br>You can rapidly check the design in the playground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br> |
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<br>Run [reasoning utilizing](https://foke.chat) guardrails with the released DeepSeek-R1 endpoint<br> |
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<br>The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends out a request to create [text based](http://lifethelife.com) 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) hub with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out [programmatically](http://182.92.169.2223000) through the SageMaker Python SDK. Let's check out both methods to assist you pick the method that [finest suits](https://gl.ignite-vision.com) 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 users will be triggered to produce a domain. |
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3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br> |
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<br>The model web browser shows available designs, with details like the company name and model capabilities.<br> |
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<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. |
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Each design card reveals crucial 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 applicable), [suggesting](http://gitlab.lecanal.fr) that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to [conjure](https://git.peaksscrm.com) up the model<br> |
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<br>5. Choose the model card to view 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 supplier 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](http://47.104.234.8512080) guidelines<br> |
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<br>Before you release the design, it's recommended to review the model details and license terms to confirm 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, utilize the automatically produced name or create a custom-made 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 number of circumstances (default: 1). |
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Selecting proper instance types and counts is vital for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. |
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10. Review all setups for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. |
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11. Choose Deploy to deploy the model.<br> |
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<br>The deployment process can take several minutes to complete.<br> |
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<br>When implementation is total, your endpoint status will alter to InService. At this point, the model is all set to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can conjure up the model using a SageMaker runtime customer 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 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing 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 additional demands 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 also utilize 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 avoid [unwanted](http://www.buy-aeds.com) charges, complete the steps in this area to clean up your resources.<br> |
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<br>Delete the Amazon Bedrock Marketplace release<br> |
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<br>If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:<br> |
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<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, [choose Marketplace](https://c3tservices.ca) deployments. |
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2. In the Managed deployments area, locate the [endpoint](http://grainfather.co.uk) 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 appropriate 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 released will sustain expenses if you leave it [running](https://activeaupair.no). 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 checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon [Bedrock Marketplace](https://git.kicker.dev) now to get started. For more details, refer to Use [Amazon Bedrock](http://39.98.84.2323000) tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart [pretrained](https://pennswoodsclassifieds.com) designs, 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](https://demo.pixelphotoscript.com) companies develop innovative services utilizing AWS services and accelerated compute. Currently, he is focused on developing techniques for fine-tuning and optimizing the inference performance of large language designs. In his spare time, Vivek takes pleasure in treking, watching motion pictures, and attempting different cuisines.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](https://jobs.campus-party.org) Specialist Solutions Architect with the Science group at AWS. His area of focus is AWS [AI](http://test.9e-chain.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br> |
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<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://chefandcookjobs.com) with the Third-Party Model [Science team](https://git.kansk-tc.ru) 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](http://daeasecurity.com) hub. She is enthusiastic about developing options that help consumers accelerate their [AI](https://usa.life) journey and unlock service value.<br> |
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