Spectral's inference economy operates as a dynamic two-sided network. On the one side, there are miners (Modelers) who train and contribute machine learning models in a privacy-preserving manner. On the other side, we have Consumers with a fundamental demand for customized, high-quality inferences.
Consumers who are unable to find a feed of inferences to solve their problem may also opt to issue a challenge of their own, becoming a Creator, staking a bounty, setting benchmarks and usually providing a dataset and a description.
Two points are worth noting in this latter aspect. The quality of inferences produced often hinges on the specific context in which they are employed. This means that relying solely on general-purpose LLMs, for instance, may fall short of meeting the precise requirements of specialized use cases. Hence, fine-tuning emerges as an important concept and need, ensuring that machine intelligence remains practical and applicable in diverse scenarios.
The fact that a network upholds the ethos of decentralization, which involves distributing trust and intellectual resources from centralized entities, does not immediately imply the quality of outputs. In this regard, Spectral adopts a meticulous design that harmoniously combines the virtues of both.
Modelers form the backbone of the protocol, playing the role of alchemists taking raw data and materializing it into valuable inferences. The permissionless nature of the network allows any sophisticated technique to underpin the network. Privacy-preserving methods protect such models, allowing perpetual incentivization for a Modeler to host and offer ongoing value to the network.
Consumers represent a diverse spectrum of entities seeking high-quality inferences. Whether it is powering smart contracts, enhancing decision-making, writing code, or enabling any other innovative applications, the demand for reliable machine intelligence is only growing. Spectral offers a solution to this demand, presenting a tapestry catering to the unique needs of each Consumer.