performance comparison
dots.llm1 outperforms mainstream open-source large language models on a number of metrics.
Key Benefits
- Chinese processing: Average score of 91.3 in Chinese tests, surpassing DeepSeek V2, V3 and AliQwen 2.5 series
- Training data: Using 11.2 trillion non-synthetic high-quality corpus, data quality is more guaranteed
- Efficiency Advantage: The MoE architecture activates only 14 billion parameters at inference time, with lower computational cost
- Context length: Extremely long context support of 32,768 tokens, ahead of most comparable models
- research value: Provide training checkpoints per 1 trillion tokens to facilitate researchers to analyze training dynamics
Application Advantages
dots.llm1 is especially optimized for Chinese language processing, which is suitable for Chinese local application scenarios. At the same time, due to the MoE architecture, it can save a lot of computing resources in practical applications, especially for dialog systems and content generation applications that need to run for a long time.
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 " What are the advantages of dots.llm1 over other open source macromodels?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil