Description
Large language models like ChatGPT have demonstrated remarkable capabilities across a variety of applications. However, their potential for enhancing the Linux development and user ecosystem remains largely unexplored. This project seeks to bridge that gap by researching practical applications of LLMs to improve workflows in areas such as backporting, packaging, log analysis, system migration, and more. By identifying patterns that LLMs can leverage, we aim to uncover new efficiencies and automation strategies that can benefit developers, maintainers, and end users alike.
Goals
- Evaluate Existing LLM Capabilities: Research and document the current state of LLM usage in open-source and Linux development projects, noting successes and limitations.
- Prototype Tools and Scripts: Develop proof-of-concept scripts or tools that leverage LLMs to perform specific tasks like automated log analysis, assisting with backporting patches, or generating packaging metadata.
- Assess Performance and Reliability: Test the tools' effectiveness on real-world Linux data and analyze their accuracy, speed, and reliability.
- Identify Best Use Cases: Pinpoint which tasks are most suitable for LLM support, distinguishing between high-impact and impractical applications.
- Document Findings and Recommendations: Summarize results with clear documentation and suggest next steps for potential integration or further development.
Resources
- Local LLM Implementations: Access to locally hosted LLMs such as LLaMA, GPT-J, or similar open-source models that can be run and fine-tuned on local hardware.
- Computing Resources: Workstations or servers capable of running LLMs locally, equipped with sufficient GPU power for training and inference.
- Sample Data: Logs, source code, patches, and packaging data from openSUSE or SUSE repositories for model training and testing.
- Public LLMs for Benchmarking: Access to APIs from platforms like OpenAI or Hugging Face for comparative testing and performance assessment.
- Existing NLP Tools: Libraries such as spaCy, Hugging Face Transformers, and PyTorch for building and interacting with local LLMs.
- Technical Documentation: Tutorials and resources focused on setting up and optimizing local LLMs for tasks relevant to Linux development.
- Collaboration: Engagement with community experts and teams experienced in AI and Linux for feedback and joint exploration.
Looking for hackers with the skills:
This project is part of:
Hack Week 24
Activity
Comments
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2 months ago by jiriwiesner | Reply
I would like to ask an LLM instance about the inner workings on the Linux kernel code. It is a common task of mine to look for a bug in a subsystem or a layer that can easily have tens of thousands of lines of code (e.g. bsc 1216813). I know having an understanding of the Linux code is what we do as developers but my understanding and knowledge is always limited because I simply do not have the time to read all of the code possibly involved in an issue. If the LLM was trained to process the source code of a specific version of Linux a developer could then ask involved questions about the code using the terms found in the code base. It should basically be something that allows a developer find the interesting parts of the code better than when using just grep.
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2 months ago by anicka | Reply
Actually, it looks like that off-the-shelf ChatGPT 4 can be already quite helpful in such tasks.
But training something like code llama on our kernels is something I indeed want to look into next time because if there is any way how to leverage LLMs in our bugfixing or backporting, this is it.
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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.
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The Context: AI + Board Games
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https://gitlab.suse.de/ngetahun/cootwbot
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Description
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User Story
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Resources
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