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Research paper by Sherline Mercy R. and collaborators, published 2026 in Zenodo (CERN European Organization for Nuclear Research). Research area: Speech and dialogue systems.
We present a systematic study of LoRA fine-tuning strategies for IBM Granite 4.0-H-Micro, a 3.2B-parameter hybrid architecture comprising 36 Mamba-2 state space layers and 4 Transformer attention layers. We evaluate four fine-tuning approaches against an unmodified baseline across three domain-specific tasks: document classification (24K examples), schema mapping (15K examples), and structured rule generation (9K examples). Our investigation proceeds in two stages. First, we find that co-training LoRA adapters with unfrozen SSM core parameters (A_log, D, dt_bias) yields consistent improvements across all tasks (V3). However, PEFT's adapter-on
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