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    Home»Machine Learning»Document RAG using Deepseek R1. Introduction | by CyberRaya | Feb, 2025
    Machine Learning

    Document RAG using Deepseek R1. Introduction | by CyberRaya | Feb, 2025

    Team_AIBS NewsBy Team_AIBS NewsFebruary 1, 2025No Comments1 Min Read
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    A user-friendly and extremely customizable Python net app designed to reveal DeepSeek-r1 in a ChatGPT-style interface, working regionally.

    Clone the GitHub repository to your native machine:

    git clone https://github.com/reflex-dev/reflex-llm-examples.git 
    cd reflex-llm-examples/chat_with_deepseek_r1_locally/deepseek_r1_chatui

    Set up the required dependencies:

    pip set up -r necessities.txt  

    Obtain and arrange the DeepSeek-r1 mannequin regionally:

    ollama pull deepseek-r1:1.5b

    Run the applying to begin chatting along with your PDF:

    reflex run --backend-host 0.0.0.0 --frontend-port 3001

    Pull the mannequin

    ollama run deepseek-r1:671b

    DeepSeek R1 represents a big development within the panorama of open-weight giant language fashions, providing a powerful stability between efficiency, effectivity, and accessibility. With its capabilities tuned for reasoning, instruction-following, and multilingual understanding, it stands as a compelling different to proprietary fashions. Its open-source nature encourages innovation, permitting builders to fine-tune and deploy it for various purposes, from chatbots to analysis assistants.

    Whereas it competes properly with present fashions by way of high quality and cost-efficiency, its long-term impression will rely upon continued enhancements in coaching methodologies, deployment optimization, and real-world adoption. Because the AI ecosystem evolves, DeepSeek R1 units a promising precedent for open-source LLMs, fostering larger transparency and collaboration within the subject.

    https://github.com/reflex-dev/reflex-llm-examples/tree/main/chat_with_deepseek_r1_locally/deepseek_r1_chatui



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