Whitepaper
  • The Schema
    • Overview
      • Welcome
      • Background
      • Vision
    • Concepts
      • Responsibilities of Roles
        • Data Uploader
        • Curator
        • Model Trainer
        • User
      • DBA and cDBA
      • Data Annotation
      • Data Bundling and Weighting
    • Product Merits
      • Full Privacy Protection
      • Guaranteed Data Ownership
      • One-Stop Service Tool
      • Positive Community Ecosystem
      • Self-Evolution of Data
      • Leveraging Cutting-Edge Technology
    • Details of Tech
      • High-Quality Data
      • Homomorphic Encryption
      • Fine Tuning
      • Human-in-the-loop
      • RLHF (Reinforcement Learning from Human Feedback)
      • MOE (Mixture of Experts)
      • Embedding
    • Tokenomics
    • Roadmap
    • Disclaimer
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  1. The Schema
  2. Concepts
  3. Responsibilities of Roles

User

PreviousModel TrainerNextDBA and cDBA

Last updated 1 year ago

In the depicted process flow, the User engages with a Fine-Tuned Model through its use and Model Quality Assessment. The model is evaluated by algorithms, and performance statistics are generated. These performance statistics lead to Remarks on DBA (Data-backed Assets), cDBA (Curated Data-backed Assets), and the Model itself. The feedback loop completes with the User receiving Airdrop/Rewards based on the model's performance, which is cross-referenced with the assessment remarks. This incentivizes the User, providing benefits that promote continued engagement with the system.

User