Identifying Disruptive Models in the Open-Source LLM Community
For researchers and practitioners in the open-source LLM ecosystem, this work provides a method to identify models that fundamentally shift technological trajectories, though the findings are largely descriptive.
This study introduces the Model Disruption Index (MDI) to identify disruptive models in the open-source LLM community, finding that most models are consolidative rather than disruptive, with disruptive models more likely to be large-scale and finetuned.
The rapid growth of open-source large language models (LLMs) has created a complex ecosystem of model inheritance and reuse. However, existing research has focused mainly on descriptive analyses of lineage evolution, with limited attention to identifying which models play a disruptive role in shaping subsequent development. Using metadata from 2,556,240 models on Hugging Face, this study reconstructs a large-scale lineage network and introduces the Model Disruption Index (MDI) to distinguish between models that reinforce existing technological trajectories and those that become new bases for later development. The results show that most models in the open-source LLM community are consolidative rather than disruptive, reflecting a highly concentrated and path-dependent evolutionary structure. Further analyses suggest that disruptive positions are more likely to emerge among large-scale models and through finetuning strategies. Overall, this study provides a new perspective for identifying disruptive models and understanding uneven technological development in open-source LLM ecosystems.