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@ -102,7 +102,7 @@ In the open-source community, 2 notable examples of fine-tuning with Mistral as
## Conclusion
The ownership and strategic use of pre-trained data serve as an invisible moat. It not only enables the tackling of complex challenges like catastrophic forgetting but also provides a baseline for continuous, targeted improvements. Although there is a solution to decomotralize, the cost remains reasonably high.
The ownership and strategic use of pre-trained data serve as an invisible moat. It not only enables the tackling of complex challenges like catastrophic forgetting but also provides a baseline for continuous, targeted improvements. Although there is a solution to decentralize, the cost remains reasonably high.
Fully open pretrained + open weight