AI & MACHINE LEARNING
Shiesty Brain Engineering
A data and model engineering case study covering audio source preparation, corpus design, QLoRA training, and local GGUF deployment. The public evidence is the complete 12 slide engineering presentation.
The problem
Specialized language model behavior depends on traceable source preparation, disciplined corpus construction, and a deployment format suited to local inference.
The solution
The project documents a pipeline from audio source preparation through dataset design and QLoRA training to a quantized local deployment artifact.
How it works
- Prepare and review source material.
- Build the training corpus and run QLoRA fine tuning.
- Export a GGUF artifact for local inference and evaluation.
My contribution
Justin’s presentation records the data preparation, training, evaluation, and local deployment workflow.
Challenges and tradeoffs
Quantization supports local execution but can change model behavior; the presentation treats evaluation as part of the engineering process.