works on
loRa 1model compression 1parameter-efficient fine-tuning 1quantization 1text-to-sql 1

From the 1 of 1 linked paper with an AI index.

collaborators

1 paper

cs.AI2026

How Small Can You Go? A Controlled Study of LoRA Rank, Target Modules, and Quantization Trade-offs for Text-to-SQL on a 60M-Parameter Model

Mahendra Singh Rathor, Anagheem Azzam

The paper investigates how LoRA rank, adapted modules, and low‑bit quantization trade off accuracy and resource usage when fine‑tuning a 60 M‑parameter T5‑small model for the WikiS…