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@rh-ai-quickstart

AI quickstart

Welcome to AI quickstart 👋

tl;dr

AI quickstarts are:

  • portable, AI-centric demos
  • focused on real business use cases
  • easily deployable in Red Hat AI environments

They illustrate what can be built on top of Red Hat AI.

Important links

User guide: https://github.com/rh-ai-quickstart/ai-quickstart-contrib/blob/main/general-faq.md

Contributor guide: https://github.com/rh-ai-quickstart/ai-quickstart-contrib/blob/main/CONTRIBUTING.md

General feedback, issues, suggestions, and membership: https://github.com/rh-ai-quickstart/ai-quickstart-contrib/issues

... a bit more detail

AI quickstarts are straight-forward, ready-to-run examples that demonstrate real business use cases on your Red Hat AI platform. They go beyond technical demos to showcase how open source AI delivers value and innovation. They are here to jump-start your journey and help you get the most out of Red Hat AI.

Many of these examples come straight from Red Hat AI experts. After all, this is our day job! Contributions from Red Hat partners and the community are not just welcome, they are encouraged.

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  5. llm-cpu-serving llm-cpu-serving Public

    Forked from RHRolun/vllm-cpu-blueprint

    This quickstart will serve a small language model on CPUs, using vLLM inference runtime

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  6. lls-observability lls-observability Public

    Llama Stack Observability quickstart

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