dots.llm1 Chinese Performance Advantages and Technical Basis
Evaluation data shows that dots.llm1 scored an average of 91.3 points in the Chinese test, significantly outperforming DeepSeek V2/V3 and Ali Qwen2.5 series models. This advantage stems from three key technical elements:
- Training data: 11.2 trillion tokens of non-synthetic high-quality corpus, rigorously screened by a three-stage processing pipeline
- Context support: 32,768 tokens of very long context window for processing long Chinese documents
- Architecture optimization: specially designed Chinese tokenizer and vocabulary to cover more than 95% Chinese expression scenarios
Practical tests show that the model's accuracy in tasks such as ancient text processing and technical document generation is 15-20% higher than that of similar models.The Xiaohongshu team adopts a dynamic course-learning strategy to enable the model to gradually master the deep features of Chinese grammar.
This answer comes from the articledots.llm1: the first MoE large language model open-sourced by Little Red BookThe
May not be reproduced without permission:AI productivity tools " dots.llm1 outperforms mainstream open source models in Chinese processing power
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