From 77aac5d8b362eddf06132dfb7b524a65a9cd2aab Mon Sep 17 00:00:00 2001 From: helenheiden559 Date: Wed, 9 Apr 2025 00:13:24 +0200 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...drock-Marketplace-And-Amazon-SageMaker-JumpStart.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md diff --git a/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md new file mode 100644 index 0000000..f2b5434 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,10 @@ +
Today, we are excited to announce 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://familyworld.io)'s first-generation frontier model, DeepSeek-R1, along with the [distilled variations](https://imidco.org) [varying](http://47.97.159.1443000) from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](http://8.137.103.221:3000) ideas on AWS.
+
In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the models also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://www.outletrelogios.com.br) that utilizes reinforcement finding out to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its reinforcement learning (RL) action, which was utilized to improve the model's reactions beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's geared up to break down complicated queries and factor through them in a detailed way. This assisted thinking process allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, and [data analysis](https://richonline.club) jobs.
+
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 appropriate professional "clusters." This method allows the design to focus on various issue domains while maintaining general efficiency. DeepSeek-R1 needs at least 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 offering 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more effective architectures based upon [popular](https://www.etymologiewebsite.nl) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more effective designs to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher design.
+
You can release 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 location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and examine models against crucial security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](http://gungang.kr) applications.
+
Prerequisites
+
To release the DeepSeek-R1 design, you [require access](https://innovator24.com) to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify 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 deploying. To ask for a limit increase, produce a limitation boost request and connect to your account team.
+
Because you will be releasing this model with [Amazon Bedrock](http://saehanfood.co.kr) Guardrails, [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile \ No newline at end of file