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<br>Today, we are thrilled 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](https://xotube.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://career.logictive.solutions) ideas 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. You can follow comparable steps to release the distilled variations of the models too.<br> |
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<br>Overview of DeepSeek-R1<br> |
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<br>DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://www.scikey.ai) that utilizes support finding out to boost thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing feature is its support learning (RL) step, which was used to fine-tune the model's reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex inquiries and factor through them in a detailed manner. This directed reasoning process enables 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 extensive capabilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as agents, sensible reasoning and data analysis 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 allows activation of 37 billion parameters, [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:AnkeStarnes867) allowing effective inference by routing questions to the most relevant specialist "clusters." This technique [enables](http://gitea.digiclib.cn801) the model to focus on various issue domains while maintaining general performance. 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 instance 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 designs bring the reasoning capabilities of the main R1 model to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.<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 model, we suggest deploying this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, [prevent harmful](https://git.pilzinsel64.de) material, and evaluate models against crucial safety criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several [guardrails tailored](https://www.ntcinfo.org) to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://social.ishare.la) 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 and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing 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 limit boost demand and connect to your account team.<br> |
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<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to utilize [guardrails](http://git.sinoecare.com) for material 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, avoid damaging material, and evaluate models against key security requirements. You can implement safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This [permits](https://4realrecords.com) you to apply guardrails to evaluate user inputs and design responses deployed on Amazon Bedrock [Marketplace](http://150.158.183.7410080) 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](https://git.alenygam.com).<br> |
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<br>The basic circulation 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 design for inference. After receiving the design'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 stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it took place at the input or [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:LeandraKrichauff) output phase. 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 gives 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 actions:<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](https://job.honline.ma) the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. |
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2. Filter for DeepSeek as a [provider](http://git.yang800.cn) and pick the DeepSeek-R1 model.<br> |
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<br>The design detail page provides vital details about the model's capabilities, pricing structure, and execution standards. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation tasks, including content development, code generation, and question answering, using its support finding out optimization and CoT thinking abilities. |
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The page likewise includes deployment options and [licensing details](https://allcallpro.com) to assist you begin with DeepSeek-R1 in your applications. |
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3. To begin utilizing DeepSeek-R1, choose Deploy.<br> |
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<br>You will be triggered to configure 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](https://video.xaas.com.vn) name (in between 1-50 alphanumeric characters). |
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5. For Number of instances, enter a variety of instances (between 1-100). |
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6. For Instance type, choose your circumstances type. For [optimum performance](http://shiningon.top) with DeepSeek-R1, a [GPU-based circumstances](http://101.34.66.2443000) type like ml.p5e.48 xlarge is suggested. |
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Optionally, you can configure advanced security and facilities settings, including virtual private cloud (VPC) networking, service role approvals, and encryption settings. For many use cases, the default settings will work well. However, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:PaulineMcLaurin) for production deployments, you may desire to review these settings to align 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 release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. |
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8. Choose Open in play ground to access an interactive user interface where you can experiment with various triggers and adjust model specifications like temperature and maximum length. |
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When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For example, content for reasoning.<br> |
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<br>This is an excellent way to check out the model's thinking and text generation abilities before incorporating it into your applications. The play ground supplies instant feedback, helping you comprehend how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal outcomes.<br> |
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<br>You can quickly check the design in the play area through the UI. However, to invoke the deployed model 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 released DeepSeek-R1 endpoint<br> |
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<br>The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing 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 created the guardrail, utilize the following code to carry out guardrails. The [script initializes](http://dkjournal.co.kr) the bedrock_runtime client, sets up inference specifications, and sends a demand to create text 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) center with FMs, [integrated](http://www5a.biglobe.ne.jp) algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production using either the UI or SDK.<br> |
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<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical approaches: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the technique that best suits your requirements.<br> |
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<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br> |
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<br>Complete the following actions to release 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 create 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 service provider 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 model 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 suitable), showing that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model<br> |
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<br>5. Choose the design card to see the design 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 supplier details. |
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Deploy button to deploy the model. |
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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 specs. |
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- Usage guidelines<br> |
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<br>Before you deploy the model, it's suggested to examine the design details and license terms to validate 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 automatically produced name or develop a custom-made one. |
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8. For Instance type ¸ select 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 circumstances types and counts is crucial for cost and [efficiency optimization](https://jobs.sudburychamber.ca). Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is [selected](https://git.opskube.com) by default. This is optimized for sustained traffic and low latency. |
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10. Review all setups for precision. For this design, 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 process can take numerous minutes to complete.<br> |
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<br>When deployment is total, your endpoint status will change to InService. At this moment, the model 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 relevant metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate 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 essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for [reasoning programmatically](http://tigg.1212321.com). The code for releasing the model is [offered](http://zerovalueentertainment.com3000) in the Github here. You can clone the note pad and range from [SageMaker Studio](http://8.137.54.2139000).<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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:<br> |
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<br>Clean up<br> |
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<br>To avoid undesirable charges, finish 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 model using 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, choose Marketplace deployments. |
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2. In the [Managed deployments](https://www.refermee.com) section, locate the endpoint you want 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 erasing 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 you deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish to stop [sustaining charges](https://www.scikey.ai). 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 deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon [Bedrock Marketplace](https://www.nikecircle.com) now to get started. 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](https://infinirealm.com).<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://git.micg.net) business develop ingenious options utilizing AWS services and accelerated compute. Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning performance of big language models. In his downtime, Vivek enjoys hiking, viewing movies, and attempting various foods.<br> |
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<br>Niithiyn Vijeaswaran is a Generative [AI](http://engineerring.net) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://git.micg.net) 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://84.247.150.84:3000) with the Third-Party Model Science group at AWS.<br> |
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<br>[Banu Nagasundaram](http://git.estoneinfo.com) leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [AI](https://okoskalyha.hu) hub. She is passionate about constructing options that help clients accelerate their [AI](https://www.ycrpg.com) journey and unlock business value.<br> |
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