DistilledGemma uses prompt engineering, QLoRA fine-tuning on a large teacher, and response-level distillation to a small student, ranking 3rd and 2nd in a 2026 historical relation extraction shared task while keeping the deployed model at ~2.3B parameters.
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DistilledGemma: Balanced Efficiency-Accuracy for Person-Place Relation Extraction from Multilingual Historical Articles
DistilledGemma uses prompt engineering, QLoRA fine-tuning on a large teacher, and response-level distillation to a small student, ranking 3rd and 2nd in a 2026 historical relation extraction shared task while keeping the deployed model at ~2.3B parameters.