-
A Complete LoRA Fine-Tuning Pipeline, End to End: Every Module 5 Concept in One Run (Ep:05.08)
Pretrain a base model on one task, freeze it, LoRA-adapt it to a second task with proper loss masking, then measure new-task accuracy, old-task interference, and exact recoverability — every…
-
Does LoRA Actually Prevent Catastrophic Forgetting? The Real Answer, Measured (Ep:05.04)
The common claim is that LoRA reduces catastrophic forgetting. Measured directly, an attached LoRA adapter can forget just as much as full fine-tuning — the real benefit is something more…
-
QLoRA Explained: Why Quantizing the Frozen Base Isn’t Quite Free (Ep:05.03)
QLoRA shrinks a frozen model’s memory footprint by storing it in 4 bits instead of 16 — but quantization noise isn’t low-rank, so a LoRA adapter sized for the task…
-
Where to Apply LoRA in a Transformer: Allocating a Fixed Rank Budget Optimally (Ep:05.02)
For a fixed total parameter budget, splitting LoRA rank equally across weight matrices is usually wrong — some matrices need far more adaptation capacity than others. Module 5 of From…
-
Choosing LoRA’s Rank: The Eckart-Young Theorem Predicts Your Exact Loss Floor (Ep:05.01)
A theorem deferred all the way back in Module 0 turns out to predict, exactly, how much loss LoRA training will plateau at for any chosen rank. Module 5 of…
-
LoRA — Adapting a Model Without Touching Most of Its Weights (Ep:05.00)
LoRA fine-tunes a 4096×4096 layer with 256x fewer trainable parameters by freezing the original weights and learning a low-rank update instead. Module 5 of From Zero to Agents derives why…
