A hands-on build guide for a LoRA adapter trainer with fused low-rank gradients and adapter-aware inference in pure NumPy — the kind of project that signals deep ML and systems expertise to hiring managers.
A hands-on guide to building a LoRA fine-tuning engine entirely in NumPy, covering low-rank decomposition, gradient computation, and adapter updates with zero deep-learning framework dependencies.
This tutorial walks you through implementing a LoRA weight merger in PyTorch, giving you a concrete portfolio piece that highlights systems thinking and ML engineering prowess.
Implement LoRA adapter injection and weight merging from scratch in pure Python, demonstrating transformer systems engineering for hiring managers.
Learn how to construct a runnable LoRA fine‑tuning side project that demonstrates production‑grade ML engineering skills and can be highlighted on your CV.