training
Growing models
In this blog, and in our Discord channel, we discuss training in detail. A topic that is often overlooked, is how to grow a model. Especially in incentivized, collaborative and distributed training, this is a Read more…
A major goal of coldint is to contribute something to the world. Miners are primarily incentivized for their contribution in the form of trained models that are publicly shared. A secondary incentive is given to miners or other contributors for sharing insights by contributing to the canonical miner codebase. This may range from pointing out bugs, to adding small features and optimizations, and to rewriting large parts of code.
By rewarding small improvements, individual miners are incentivized to regularly share their improved work. This in turn enables other miners to build upon earlier contributions, accelerating the collective effort. Code contributions that enable combining individual training efforts into an even better combined model will be highly rewarded, as this will truly unlock the collective potential.
Within coldint, iterations are done in three main areas:
All stakeholders can participate in improving each of these areas. This leads to a dynamic community which promotes the exchange of ideas, insights and knowledge.
Coldint strives to be transparent by publicizing miner code, training objective schedules, awarded bounties and research results. Coldint invites community members to suggest innovative training goals. Mitigating steps are taken to prevent well known “metagaming” tactics from earning any reward. If new gaming opportunities arise that would lead to misalignment between miner and subnet objective, bug bounties should ensure that reporting bugs is as lucrative as exploiting them. Bug bounties are rewards for code contributions, and will be publicized to elicit additional bug reports and fixes.
Various subnet design choices made will be discussed in the Discord channel and in blog posts on this site.
In this blog, and in our Discord channel, we discuss training in detail. A topic that is often overlooked, is how to grow a model. Especially in incentivized, collaborative and distributed training, this is a Read more…
This post discusses one of the essential ingredients for training a model: how to see whether your model is actually improving, in other words, how do you benchmark the result of your training? Please read Read more…
In the previous post on incentive we described how loss values are used to score models against each other, and how the relative standing between models changes over time due to decaying advantage. From that Read more…