Tag
This paper introduces an exact generate–transform decomposition to analyze how small 7–9B LLM teams scale across eight orchestration architectures, finding that scaling returns are sharply task-dependent — Proposer-Critic wins on arithmetic benchmarks but no architecture dominates across all tasks.
The author explores using small LLMs like Gemma 4 as classifiers by analyzing logit probabilities with high temperature and calibration, sharing code and findings on GitHub.
Presents FiT, a diagnostic framework to evaluate small LLMs on cybersecurity QA capabilities before fine-tuning, showing that fine-tuning can degrade vocabulary and parametric knowledge depending on the regime. Provides guidance to avoid unnecessary fine-tuning.
Fine-tuning small LLMs (3B-7B) with QLoRA on biomedical claim verification achieves higher F1 than GPT-4o and GPT-5 at 44.5x lower cost, and reveals a structural artifact in SciFact. The study demonstrates robust cross-domain transfer when training on structurally sound data.
A detailed benchmark of 20 small LLMs quantized for a 6GB GPU, measuring speed and VRAM usage at various context lengths, with qualitative probing for tool-use and instruction following. The report aims to help users with modest hardware choose models for local, private automation tasks.