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To mark the model’s 60th anniversary, which was celebrated last year, the museum has published a new book offering a behind-the-scenes look at how the Panorama was made. Original footage of the last major update to the model, completed in 1992, has also gone on show at the museum as part of a 12-minute video that features interviews with some of the renovators.

The Queens Museum’s assistant director of archives and collections, Lynn Maliszewski, who took CNN on a visit of the Panorama in early March, said she hopes the book and video will help to draw more visitors and attention to the copious amount of labor — over 100 full-time workers, from July 1961 to April 1964 — that went into building the model.

“Sometimes when I walk in here, I get goosebumps, because this is so representative of dreams and hopes and family and struggle and despair and excitement… every piece of the spectrum of human emotion is here (in New York) happening at the same time,” said Maliszewski. “It shows us things that you can’t get when you’re on the ground.”
Original purpose
The Panorama was originally built for the 1964 New York World’s Fair, then the largest international exhibition in the US, aimed at spotlighting the city’s innovation. The fair was overseen by Robert Moses, the influential and notorious urban planner whose highway projects displaced hundreds of thousands New Yorkers. When Moses commissioned the Panorama, which had parts that could be removed and redesigned to determine new traffic patterns and neighborhood designs, he saw an opportunity to use it as a city planning tool.

Originally built and revised with a margin of error under 1%, the model was updated multiple times before the 1990s, though it is now frozen in time. According to Maliszewski, it cost over $672,000 to make in 1964 ($6.8 million in today’s money) and nearly $2 million (about $4.5 million today) was spent when it was last revised in 1992.

WilliamBoype

28 de março de 2025 |

A librarian ran off with a yacht captain in the summer of 1968. It was the start of an incredible love story
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31 de março de 2025 |

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31 de março de 2025 |

31 de março de 2025 |

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31 de março de 2025 |

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31 de março de 2025 |

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погана кредитна історія де взяти кредит

1 de abril de 2025 |

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My blog post https://fina.guru/blog/vsinovikredytyzpoganoyuistorieu

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1 de abril de 2025 |

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Para los propietarios de emprendimientos, la financiamiento en unidades de ajuste y dispositivos puede ser fundamental para incrementar la productividad y eficiencia de sus sistemas. Esto es sobre todo importante para los inversores que dirigen modestas y medianas empresas, donde cada detalle importa.

1 de abril de 2025 |

Базируясь в Москве на Озерковском переулке, 12, компания предоставляет полный спектр услуг для задач строительства и перевозок. Возможность быстро откликаться на заявки обеспечивается парком свыше 150 единиц спецтехники, каждая из которых проходит плановое техническое обслуживание.

Особенности предоставления услуг:

Скорость работы:
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Автокраны:
Диапазон грузоподъемности 3-12 тонн
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Гарантированная доставка за 120 минут

Землеройная техника:
Возможность копания на глубину 6.5 метров
Возможность перемещения со скоростью 41 км/ч
Емкость ковша до 1.3 м?

Перевозочные средства:
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Вместимость кузова 2-92 м?
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Главные особенности:
Современный автопарк последних модификаций
Полное документальное сопровождение согласно законодательству РФ
Работа с НДС и возможность постоплаты
Полисы страхования на все виды услуг
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VivianRousy

4 de abril de 2025 |

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Merlesit

4 de abril de 2025 |

«BLACKSPRUT Test» – это сервис в рамках проекта «BLACKSPRUT Control», призванный обеспечить максимальный контроль качества представленных на площадке товаров. Ключевая задача работы подразделения «BLACKSPRUT Test» – выявление низкокачественной продукции через проведение проверок и лабораторных исследований и ликвидация товаров ненадлежащего качества. Ведь одна из первоочередных задач, которая заявлена в «Идеологическом меморандуме BLACKSPRUT» – это минимизация рисков.

4 de abril de 2025 |

5 de abril de 2025 |

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ai

6 de abril de 2025 |

Today, we are excited to reveal 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’s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative AI concepts on AWS.

In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models also.

Overview of DeepSeek-R1

DeepSeek-R1 is a big language design (LLM) established by DeepSeek AI that uses reinforcement finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating function is its support knowing (RL) step, which was utilized to improve the design’s reactions beyond the standard pre-training and tweak process. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it’s geared up to break down complicated inquiries and reason through them in a detailed way. This guided reasoning process permits the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the market’s attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, sensible thinking and information interpretation tasks.

DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient reasoning by routing inquiries to the most appropriate professional “clusters.” This method allows the design to focus on different problem domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.

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 describes a process of training smaller, more efficient models to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.

You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and evaluate designs against essential security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To inspect 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 usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit boost, create a limitation boost demand and connect to your account team.

Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to utilize guardrails for content filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging content, and evaluate models against essential security requirements. You can implement security steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model reactions deployed 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 produce the guardrail, see the GitHub repo.

The basic flow includes 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 reasoning. After receiving the model’s output, another guardrail check is applied. If the output passes this final 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 occurred at the input or output stage. The examples showcased in the following areas show inference utilizing this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:

1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.

The design detail page supplies essential details about the model’s capabilities, rates structure, and execution guidelines. You can find detailed use instructions, consisting of sample API calls and code bits for combination. The model supports numerous text generation jobs, including content creation, code generation, and question answering, using its reinforcement learning optimization and CoT thinking capabilities.
The page likewise includes implementation alternatives and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.

You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Number of circumstances, get in a variety of circumstances (in between 1-100).
6. For Instance type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you may want to examine these settings to line up with your company’s security and compliance requirements.
7. Choose Deploy to start utilizing the design.

When the deployment is total, you can evaluate DeepSeek-R1’s capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive user interface where you can experiment with different triggers and change design criteria like temperature level and optimum length.
When utilizing R1 with Bedrock’s InvokeModel and Playground Console, utilize DeepSeek’s chat design template for ideal outcomes. For example, material for reasoning.

This is an excellent way to check out the design’s reasoning and text generation capabilities before integrating it into your applications. The play area supplies immediate feedback, assisting you comprehend how the model reacts to different inputs and letting you fine-tune your prompts for optimal results.

You can rapidly test the design in the play ground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.

Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example shows how to carry out inference using a released 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 actually produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning specifications, and sends a demand to produce text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production utilizing either the UI or SDK.

Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free methods: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s explore both techniques to assist you select the method that best matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:

1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.

The design browser shows available models, with details like the company name and model capabilities.

4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card shows crucial details, including:

– Model name
– Provider name
– Task classification (for example, Text Generation).
Bedrock Ready badge (if applicable), indicating that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model

5. Choose the design card to see the design details page.

The model details page includes the following details:

– The model name and service provider details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details

The About tab includes crucial details, such as:

– Model description.
– License details.
– Technical requirements.
– Usage standards

Before you release the design, it’s suggested to evaluate the design details and license terms to confirm compatibility with your usage case.

6. Choose Deploy to proceed with deployment.

7. For Endpoint name, use the immediately generated name or develop a custom one.
8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of circumstances (default: 1).
Selecting appropriate instance types and counts is vital for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the design.

The deployment process can take several minutes to finish.

When deployment is complete, your endpoint status will alter to InService. At this point, the model is all set to accept inference requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is total, you can conjure up the model using a SageMaker runtime client and integrate it with your applications.

Deploy DeepSeek-R1 utilizing the SageMaker Python SDK

To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.

You can run additional requests against the predictor:

Implement guardrails and run inference with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can likewise utilize 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 revealed in the following code:

Tidy up

To prevent undesirable charges, finish the actions in this section to tidy up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you released the design utilizing Amazon Bedrock Marketplace, complete the following actions:

1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments.
2. In the Managed releases section, locate the endpoint you want to delete.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you’re deleting the right deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.

Conclusion

In this post, we explored how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.

About the Authors

Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies build innovative solutions using AWS services and sped up calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the reasoning efficiency of large language models. In his downtime, Vivek delights in treking, enjoying movies, and attempting various cuisines.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor’s degree in Computer technology and Bioinformatics.

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