commit d4e08d9296ebfba08f13c011a8fb93daa377a042 Author: francesconls7 Date: Sun Feb 16 07:11:31 2025 +0100 Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' 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..b19719c --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,9 @@ +
Today, we are delighted to announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, [it-viking.ch](http://it-viking.ch/index.php/User:SheenaWhalen2) you can now release DeepSeek [AI](https://sundaycareers.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and properly scale your generative [AI](https://www.dadam21.co.kr) ideas on AWS.
+
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](http://git.520hx.vip:3000) that utilizes support learning to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing feature is its support knowing (RL) action, which was utilized to refine the model's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, [implying](https://git.soy.dog) it's geared up to break down intricate inquiries and reason through them in a detailed manner. This assisted thinking procedure permits 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 concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, logical thinking and data interpretation tasks.
+
DeepSeek-R1 utilizes a Mix of [Experts](https://gitea.moerks.dk) (MoE) architecture and [yewiki.org](https://www.yewiki.org/User:CindaNangle760) is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, enabling efficient inference by routing questions to the most relevant expert "clusters." This approach permits the design to specialize in different issue domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will [utilize](https://code.paperxp.com) 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 designs bring the reasoning capabilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, [wiki-tb-service.com](http://wiki-tb-service.com/index.php?title=Benutzer:Milla01Z3855169) and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to imitate the behavior and thinking patterns of the larger DeepSeek-R1 model, utilizing it as an instructor model.
+
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate models against crucial safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security [controls](https://gogs.dzyhc.com) across your generative [AI](https://www.activeline.com.au) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas [console](http://www.grainfather.eu) and under AWS Services, select Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile \ No newline at end of file