Vanna Labs
  • Introduction
    • 👋Welcome
    • ✨Summary
  • Vision
    • 🏪The AI One-Stop-Shop
      • AI Models on Vanna
      • Applied ML on Vanna
    • 🤝Interoperability
    • ✅Computational Verifiability
    • 🌐Decentralization
    • 🛡️Censorship Resistance
  • Vanna Network
    • 🏗️Architecture
      • Data Preprocessing
      • Parallelized Inference Pre-Execution (PIPE)
      • Validation-Computation Separation (VCS)
      • Cryptoeconomic Security
      • Modular Design
    • ⚙️Inference Modes
      • Vanilla Inference
      • zkML Inference
      • zkFP Inference
      • opML Inference
      • TEE Inference
    • 💿Data Storage
      • Model Storage
      • Data Availability
    • 💻Portal
  • Build
    • 🛠️Getting started
      • Networks
      • Faucets
      • Model Storage
    • 💻Building dApps
      • Data Preprocessing
      • Inference API
      • Inference Example
    • 💾Models
      • Supported LLMs
  • 🔗Links
  • Key Concepts
    • 🧠AI x Blockchain
    • 📜Optimistic Rollups
    • 💾Data Availability
    • ⚡zkML
    • ☀️opML
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  1. Vision

Decentralization

Decentralizing and open-sourcing model development

PreviousComputational VerifiabilityNextCensorship Resistance

Last updated 1 year ago

As opposed to the status quo of closed-source model development, Vanna Labs believes in open-sourcing models through its decentralized platform. When researchers and developers share their models, algorithms, and findings with the global community, it fosters collaboration, innovation, and the rapid exchange of ideas.

Vanna's models will be stored in IPFS, a P2P decentralized storage network. Any participant is able to run an IPFS storage node and access the models through the model's public CID (content identifier).

Note: In the future, Vanna will develop technical solutions that allow for private model storage.

The open-source approach allows a diverse range of contributors, often from different backgrounds and organizations, to collectively improve upon existing models. This collaborative effort not only accelerates the development of AI technologies but also enables the community to address challenges and iterate on solutions more efficiently.

Moreover, open-sourcing promotes transparency, as the inner workings of models become accessible to other developers. This transparency not only facilitates trust and understanding among users but also encourages scrutiny and constructive feedback from the broader community. The iterative feedback loop created through open-source development can lead to the identification and resolution of issues more swiftly, ultimately resulting in more robust and reliable AI models.

Additionally, the democratization of AI knowledge through open-source initiatives helps democratize access to cutting-edge technologies, leveling the playing field for researchers and practitioners around the world, and contributing to the democratization of artificial intelligence itself. In summary, the open-sourcing of AI model development and research acts as a catalyst for collective advancement, fostering innovation, transparency, and a faster pace of development.

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