Build a LoRA Adapter Layer from Scratch in PyTorch
A hands‑on guide to building a LoRA adapter layer in pure PyTorch, with complete code, merge/unmerge, and tips for turning the project into a CV‑standing side project.
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Build a LoRA Adapter from Scratch: A Portfolio-Ready PyTorch Project
A hands-on build guide for implementing a LoRA adapter from scratch in PyTorch, including rank-decomposed weight matrices and a complete fine-tuning loop for BERT — designed to signal real ML engineering skill on a portfolio.
Building a LoRA Training Loop From Scratch in Pure NumPy
Build a working LoRA fine-tuning loop in pure NumPy, computing every gradient by hand through low-rank adapters. A practical side project that demonstrates real systems understanding to hiring managers.
LoRA vs QLoRA: A Practical Guide to Efficient LLM Fine‑Tuning
Introduction As large language models (LLMs) have grown into the tens and hundreds of billions of parameters, full fine‑tuning has become prohibitively expensive for most practitioners. Two techniques—LoRA and QLoRA—have emerged as leading approaches for parameter-efficient fine‑tuning (PEFT), enabling high‑quality adaptation on modest hardware. They are related but distinct: LoRA (Low-Rank Adaptation) introduces small trainable matrices on top of a frozen full‑precision model. QLoRA combines 4‑bit quantization of the base model with LoRA adapters, making it possible to fine‑tune huge models (e.g., 65B) on a single 24–48 GB GPU. This article walks through: ...