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PublishingResearch ArticleUnder peer review

Quantisation-Aware Distillation for On-Device Assistants

Daniel Okafor, Yuki Saito, Priya Venkataraman

Version 1 · 1 reviewer report · 28 August 2026 · 10.xxxx/ctm.pb.2026.0006

Running assistant-grade language models on phones requires both small models and low-precision arithmetic, yet distillation and quantisation are usually applied one after the other. We train a 1.3B-parameter student with distillation losses computed through simulated 4-bit quantisation, so that the student learns to be robust to its own rounding error. Compared with distilling first and quantising afterwards, the joint procedure recovers 41% of the accuracy lost to 4-bit quantisation, reaches 94% of the full-precision student on a suite of assistant tasks, and runs 3.1× faster on a mid-range mobile processor.

Abstract PDF 2,144 7862 cited

PublishingResearch ArticlePeer reviewed

Scaling Behaviour of Parameter-Efficient Fine-Tuning Across Model Sizes

Priya Venkataraman, Daniel Okafor

Version 2 · 3 reviewer reports · 6 March 2026 · 10.xxxx/ctm.pb.2026.0002

Parameter-efficient fine-tuning (PEFT) methods adapt large models by updating a small fraction of weights, but most evidence comes from a single model size. We fit scaling curves for low-rank adaptation across six base-model sizes spanning two orders of magnitude and four downstream tasks. The optimal adapter rank grows sub-linearly with model width, and the gap between PEFT and full fine-tuning shrinks as models grow, closing to within 0.6 points at the largest size. A simple rule that sets the rank proportional to the square root of the hidden dimension recovers 96% of the best rank found by grid search. We provide fitted coefficients, discuss threats to validity, and note that results on instruction-following tasks differ from those on classification.

Abstract PDF 3,902 1,5445 cited