SN9 is Bittensor's pre-training subnet, managed by Macrocosmos
It's designed to provide pre-trained models to the Bittensor ecosystem, incentivising miners to build the best models.
How SN9 trains SOTA Intelligence
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Incentivising quality output
SN9 incentivises miners to provide the highest quality pre-training models and validators that assess the quality of the miner's models. Quality is assessed by the validators against a standard agreed by the community.
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Identify AND reward contributions
A winner-takes-all model stimulates minors to continuously compete, so that their model has the best chance of being number one. This system encourages miners to commit resources and compute power.
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De-risk influence of bad actors
The other consequence of a winner-takes-all compensation structure is that it encourages greater professionalism among miners, while discouraging cabbalistic behavior. Together with robust validation mechanisms, SN9's design ensures that it pays to play by the rules.
For more information, visit the Pretraining Subnet GitHub Repository.