KoQuality-Polyglot-5.8b is a fine-tuned iteration of the EleutherAI/polyglot-ko-5.8b model, specifically trained on the KoQuality dataset. Notably, when excluding models employing COT datasets, KoQuality-Polyglot-5.8b exhibits exceptional performance in same size models, even though it operates with a relatively small dataset. Our approach centers around leveraging high-quality instruction datasets to deepen our understanding of commands, all the while preserving the performance of the Pre-trained Language Model (PLM). Compared to alternative models, we have achieved this with minimal learning, utilizing only 1% of the dataset, which equates to 4006 instructions. We use KoBEST benchmark datasets(BoolQ, COPA, HellaSwag, SentiNeg, WiC) to compare the performance of our best model and other models accuracy. Our model outperforms other models in the average accuracy score of the KoBEST datasets. — learningrate: 5e-5 — trainbatchsize: 4 — seed: 42 — distributedtype: multi-GPU (A100 80G) + No offloading — numdevices: 4 — gradientaccumulationsteps: 16 — optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 — lrschedulertype: linear — numepochs: 2.0 — Transformers 4.30.2 — Pytorch…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: DILAB-HYU
Теги: gpt_neox, generated_from_trainer, polyglot-ko, gpt-neox, KoQuality, ko, text-generation-inference, endpoints_compatible
Лайков: 3 | Загрузок: 17
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
