#qlora

2 articles

01 ·Training & Alignment·FREE·PAPER·19 min read QLoRA — Fine-Tuning a 65B Model on One GPU A 65-billion-parameter model in 16-bit weights is 130 GB before you have trained anything. QLoRA gets that onto a single 48 GB card by storing the frozen weights in a 4-bit format shaped like the weights themselves, quantizing the quantization constants, and paging the optimizer to CPU — while gradients still flow through those 4-bit weights into a small 16-bit adapter. 02 ·Time Series·★ MEMBER·PAPER·13 min read Paper Review: MetroLLM-Bench — What Happens When an LLM Runs the Ticket Machine. Why a 2.6 GB 4B Model Tied GPT-5.6, and Why Fine-Tuning Hurt at 27B A 955-case benchmark that asks how well a language model can serve as the decision layer of a transit ticket kiosk. We read, from the paper alone, how a 4B QLoRA student beat GPT-5.6 on the deterministic tier and why PEFT gains shrink with model size until they turn negative at 27B.