MiniPLM-Mamba-130M is a 130M parameter language model with the Mamba architecture pre-trained from scratch on the Pile using the MiniPLM knowledge distillation framework. It uses the official Qwen1.5-1.8B as the teacher model. This model demonstrates the flexibility of the MiniPLM framework in conducting knowledge distillation across model families. The pre-training corpus refined by Difference Sampling in MiniPLM is open-sourced for reproducibility. Knowledge distillation (KD) is widely used to train small, high-performing student language models (LMs) using large teacher LMs. While effective in fine-tuning, KD during pre-training faces challenges in efficiency, flexibility, and effectiveness. Existing methods either incur high computational costs due to online teacher inference, require tokenization matching between teacher and student LMs, or risk losing the difficulty and diversity of the teacher-generated training data. To address these issues, MiniPLM is proposed, a KD framework for pre-training LMs by refining the training data distribution with the teacher’s knowledge. For efficiency, MiniPLM performs offline teacher LM inference, allowing KD for multiple student LMs…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: MiniLLM
Теги: mamba, en, text-generation-inference, endpoints_compatible
Лайков: 4 | Загрузок: 67
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
