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 deploy DeepSeek AI's first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative AI concepts on AWS.
In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the models too.
Overview of DeepSeek-R1
DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that utilizes support finding out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating function is its support learning (RL) step, which was used to fine-tune the design's responses beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, implying it's equipped to break down complex queries and reason through them in a detailed manner. This assisted thinking procedure enables the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, rational thinking and data analysis tasks.
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, making it possible for effective reasoning by routing questions to the most pertinent professional "clusters." This approach enables the design to focus on different issue domains while maintaining overall efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for archmageriseswiki.com reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile
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DeepSeek R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart
Cruz Ashmore edited this page 6 months ago