BeagleMind: Model Fine-tuning for offline troubleshooting
Project Overview:
BeagleMind is an AI-powered assistant designed to help BeagleBoard users troubleshoot hardware and software issues. This Google Summer of Code (GSoC) project focuses on creating a specialized, fine-tuned language model that can operate entirely offline, providing BeagleBoard community members with instant, accurate technical support without requiring internet connectivity.
Goal: Empower users to troubleshoot BeagleBoard hardware offline with a custom QA model Stretch Goal: Apply quantization to the fine-tuned model so it can run efficiently on embedded and resource-constrained devices, enabling offline troubleshooting even on low-power hardware. Software Skills: Python, supervised fine-tuning, LLMs, data preprocessing, model evaluation, deployment, PyTorch/TensorFlow Hardware Skills: Quantization techniques, resource-constrained inference, hardware acceleration (optional) Possible Mentors: Jason Kridner, Fayez Zouari Possible Project Size: Medium (175 Hours)
​My name is Lawson Eburu, and I am a final-year Computer Engineering student. I’m writing to express my strong interest in the BeagleMind Model Fine-Tuning project for GSoC 2026.
My background fits this “bridge” well:
​Machine Learning: I’m active in competitive ML on Zindi, which has given me significant experience in data preprocessing and model evaluation—two core requirements for the BeagleMind QA dataset.
​LLM Development: I have experience working with LLMs and am familiar with Supervised Fine-Tuning (SFT) and PyTorch.
​Hardware Awareness: I am currently studying Verilog and FPGA logic synthesis. While this project is software-focused, my hardware background helps me understand the resource constraints and the specific troubleshooting pain points BeagleBoard users face.
​I’d love to explore using llama.cpp to ensure the fine-tuned model runs efficiently on boards like the BeagleBone AI-64.
​I’ve started looking through the existing documentation and forum archives to understand the potential data sources. Are there specific datasets or previous BeagleMind iterations you would recommend I look into?
Thank you for the reply.
I would like to talk a bit about the current state of BeagleMind.
For the moment, the project has a CLI called BeagleMind-CLI (available on PyPI), and a user interface available on mind.beagleboard.org. In the previous GSoC, RAG was the main target and showed a very decent performance. This year we would like to go with the Model Fine tuning approach for a much more reliable model that can also run on the user machine efficiently.
For now, we have a dataset of QA pairs generated using easy-dataset that cover the official docs, a dataset of raw forum threads + replies and raw discord messages for the past 2 years.
You can check the gsoc discord channel for more details.
Also, you can have a look on the new release of BitNet.cpp, you can check it out here BitNet CPU Inference Optimization, does it solve our issue with LLM local inference or not?
What is the maximum size of LLM that we can realistically run? For example, Gemma 3 27B supports image processing, which could be useful for visual understanding tasks. However, if we move in that direction, it may be challenging to obtain clear and accurate pin-connection details from end users through images alone.
Other than this, we could implement a local script that automatically scans the system and gathers all I/O and peripheral information. Integrating this with BeagleMind would significantly improve the debugging workflow, as users would not need to repeatedly and explicitly provide the same hardware details during each interaction.
Using BitNet.cpp and BitNet Model is a great idea but, as per my understanding, BitNet.cpp is primarily an inference engine designed to run BitNet-based models. Therefore, using it would require training or obtaining a compatible BitNet model architecture. Building such a model from scratch would likely be infeasible within the current project scope. Instead, it may be more practical to use an existing pretrained model, fine-tune it on the BeagleBoard dataset, and apply quantization techniques to ensure efficient offline inference while maintaining accuracy.