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Found 2 papers, 1 papers with code
ReTok: Replacing Tokenizer to Enhance Representation Efficiency in Large Language Model
In this work, we propose a method to improve model representation and processing efficiency by replacing the tokenizers of LLMs.
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
In this paper, we present AquilaMoE, a cutting-edge bilingual 8*16B Mixture of Experts (MoE) language model that has 8 experts with 16 billion parameters each and is developed using an innovative training methodology called EfficientScale.
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