Project Description
The goal is to have a language model, that is able to answer technical questions on Uyuni. Uyuni documentation is too large for in-context processing, so finetuning is the way to go.
Goal for this Hackweek
Finetune a model based on llama-2-7b.
Resources
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This project is part of:
Hack Week 23
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Description
Start experimenting the generative SUSE-AI chat bot, asking questions on different areas of knowledge or science and possibly analyze the quality of the LLM model response, specific and comparative, checking the answers provided by different LLM models to a same query, using proper quality metrics or tools or methodologies.
Try to define basic guidelines and requirements for quality test automation of AI-generated responses.
First approach of investigation can be based on manual testing: methodologies, findings and data can be useful then to organize valid automated testing.
Goals
- Identify criteria and measuring scales for assessment of a text content.
- Define quality of an answer/text based on defined criteria .
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- Manually run query session and apply evaluation criteria to answers.
- Draft requirements for test automation of AI answers.
Resources
- Announcement of SUSE-AI for Hack Week in Slack
- Openplatform and related 3 LLM models gemma:2b, llama3.1:8b, qwen2.5-coder:3b.
Notes
Foundation models (FMs):
are large deep learning neural networks, trained on massive datasets, that have changed the way data scientists approach machine learning (ML). Rather than develop artificial intelligence (AI) from scratch, data scientists use a foundation model as a starting point to develop ML models that power new applications more quickly and cost-effectively.Large language models (LLMs):
are a category of foundation models pre-trained on immense amounts of data acquiring abilities by learning statistical relationships from vast amounts of text during a self- and semi-supervised training process, making them capable of understanding and generating natural language and other types of content , to perform a wide range of tasks.
LLMs can be used for generative AI (artificial intelligence) to produce content based on input prompts in human language.
Validation of a AI-generated answer is not an easy task to perform, as manually as automated.
An LLM answer text shall contain a given level of informations: correcness, completeness, reasoning description etc.
We shall rely in properly applicable and measurable criteria of validation to get an assessment in a limited amount of time and resources.
Run local LLMs with Ollama and explore possible integrations with Uyuni by PSuarezHernandez
Description
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Our project focused on modeling and analyzing two card games of our own design within the TAG framework:
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A family picture of our card games in progress. From the top: Bamboo, Totoro, R3
Results: Learning, Collaboration, and Innovation
Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:
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Saline (state deployment control and monitoring tool for SUSE Manager/Uyuni) by vizhestkov
Project Description
Saline is an addition for salt used in SUSE Manager/Uyuni aimed to provide better control and visibility for states deploymend in the large scale environments.
In current state the published version can be used only as a Prometheus exporter and missing some of the key features implemented in PoC (not published). Now it can provide metrics related to salt events and state apply process on the minions. But there is no control on this process implemented yet.
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Description
While we currently have extensive documentation on user-oriented tasks such as adding minions, patching, fine-tuning, etc, there is a notable gap when it comes to centralizing and documenting core functionalities for developers.
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Description
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Create a python and/or golang utility that will manage users in SUSE Manager based on LDAP/AD group-membership. In a configuration file is defined which roles the members of a group will get.
Table of contents
Installation
To install this project, perform the following steps:
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bash
zypper in python3 python3-PyYAML
pip install yaml
On the server or PC, where it should run, create a directory. On linux, e.g. /opt/sm-ldap-users
Copy all the file to this directory.
Edit the configsm.yaml. All parameters should be entered. Tip: for the ldap information, the best would be to use the same as for SSSD.
Be sure that the file sm-ldap-users.py is executable. It would be good to change the owner to root:root and only root can read and execute:
bash
chmod 600 *
chmod 700 sm-ldap-users.py
chown root:root *
Usage
This is very simple. Once the configsm.yaml contains the correct information, executing the following will do the magic:
bash
/sm-ldap-users.py
repository link
https://github.com/mbrookhuis/sm-ldap-users
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Make it really easy for anyone to run the Uyuni containerized server on whatever OS they want (with support for containers of course).
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Description
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Goals
- Explore Ollama
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Resources
- https://ollama.com/
- https://huggingface.co/
- https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/