Run local LLMs with Ollama and explore possible integrations with Uyuni
a project by PSuarezHernandez
Updated 4 months ago. 4 hacker ♥️. 1 follower.

Description

Using Ollama you can easily run different LLM models in your local computer. This project is about exploring Ollama, testing different LLMs and try to fine tune them. Also, explore potential ways of integration with Uyuni.

Goals

  • Explore Ollama
  • Test different models
  • Fine tuning
  • Explore possible integration in Uyuni

Resources

  • https://ollama.com/
  • https://huggingface.co/
  • https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/

Join this project

Looking for hackers with the skills:

uyuni llm ollama python ai

This project is part of:

Hack Week 24

Activity

  • 4 months ago: juliogonzalezgil liked this project.
  • 4 months ago: frantisek.simorda liked this project.
  • 4 months ago: j_renner liked this project.
  • 4 months ago: PSuarezHernandez added keyword "uyuni" to this project.
  • 4 months ago: PSuarezHernandez added keyword "llm" to this project.
  • 4 months ago: PSuarezHernandez added keyword "ollama" to this project.
  • 4 months ago: PSuarezHernandez added keyword "python" to this project.
  • 4 months ago: PSuarezHernandez added keyword "ai" to this project.
  • 4 months ago: PSuarezHernandez liked this project.
  • 4 months ago: PSuarezHernandez started this project.
  • 4 months ago: PSuarezHernandez originated this project.


    • Comments

    • PSuarezHernandez
      3 months ago by PSuarezHernandez | Reply

      Some conclusions after Hackweek 24:

      • ollama + open-webui is a nice combo to allow running LLMs locally (tried also Local AI)
      • open-webui allows you to add custom knoweldge bases (collections) to feed models.
      • Uyuni documentation, Salt documentation can be used on this collections to make models to learn.
      • Using a tailored documentation works better to feed models.
      • Tried different models: llama3.1, mistral, mistral-nemo, gemma2, phi3,..
      • Getting promising results, particularly with mistral-nemo.. but also getting model hallutinations - model parameters can be adjusted to reduce them.

      Takeaways

      • Small models runs fairly well with CPU only.
      • Making an expert assistance on Uyuni, with an extensive knowledge based on documentation, might be something to keep exploring.

      Next steps

      • Make the model to understand Uyuni API, so it is able to translate user requests to actual call to Uyuni API.

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    Automated Test Report reviewer by oscar-barrios

    Description

    In SUMA/Uyuni team we spend a lot of time reviewing test reports, analyzing each of the test cases failing, checking if the test is a flaky test, checking logs, etc.

    Goals

    Speed up the review by automating some parts through AI, in a way that we can consume some summary of that report that could be meaningful for the reviewer.

    Resources

    No idea about the resources yet, but we will make use of:

    • HTML/JSON Report (text + screenshots)
    • The Test Suite Status GithHub board (via API)
    • The environment tested (via SSH)
    • The test framework code (via files)