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Beyond Open Source AI: How Bagel’s Cryptographic Architecture, Bakery Platform, and ZKLoRA Drive Sustainable AI Monetization Asif Razzaq Artificial Intelligence Category – MarkTechPost

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Bagel is a novel AI model architecture that transforms open-source AI development by enabling permissionless contributions and ensuring revenue attribution for contributors. Its design integrates advanced cryptography with machine learning techniques to create a trustless, secure, collaborative ecosystem. Their first platform, Bakery, is a unique AI model fine-tuning and monetization platform built on the Bagel model architecture. It creates a collaborative space where developers can fine-tune AI models without compromising the privacy of their proprietary resources or exposing sensitive model parameters.

Origin and Vision

The idea for Bagel emerged from its founder, Bidhan Roy, who has a rich engineering and machine learning background and has contributed to the world’s largest ML infrastructures at Amazon Alexa, Cash App, and Instacart. Recognizing the unsustainability of open-source AI as a charitable model, Roy envisioned a system that would incentivize contributors by making their work monetizable. His introduction to cryptography during his work on Cash App’s Bitcoin trading platform in 2017 became the foundation for Bagel’s innovative approach to combining cryptographic methods with AI development.

Bagel’s unique value proposition is built around three core pillars:

  1. Attribution: Bagel ensures that every structural or parametric contribution is verifiably attributed using its novel ZKLoRA method, providing a transparent trail of creative work and fostering accountability in collaborative AI development.  
  2. Ownership: Contributors retain perpetual claims on their innovations through privacy-preserving containers and parameter obfuscation, eliminating the need for traditional licensing agreements while safeguarding intellectual property.  
  3. Privacy: Secure model encapsulation and layered obfuscation protect proprietary components, preventing unauthorized access even in untrusted or outsourced compute environments, ensuring privacy and trust throughout the development process. 

Core Innovations of Bagel

  • Permissionless Contributions: Bagel allows developers, researchers, and resource owners to contribute to AI model development without requiring explicit permissions or prior agreements. This decentralized approach eliminates barriers to entry.
  • Revenue Attribution: Bagel’s unique feature is its ability to attribute and distribute revenue to all ecosystem contributors fairly. The platform accurately tracks contributions and model enhancements using cryptographic techniques, ensuring that contributors are rewarded proportionately.
  • Cryptography Meets Machine Learning: Bagel’s innovative architecture relies on a fusion of cryptographic methods and machine learning advancements, including:
    • Parameter-Efficient Fine-Tuning (PEFT): It optimizes model fine-tuning processes, reducing resource requirements while maintaining performance.
    • ZKLoRA: Bagel Research Team’s latest innovation – a zero-knowledge protocol that verifies LoRA updates for base model compatibility without exposing proprietary data, ensuring secure and efficient collaboration.

Bagel’s architecture is implemented through its platform, Bakery. It enables decentralized AI development by allowing developers to contribute models and optimizations securely, dataset providers to share proprietary data privately using cryptographic methods, and resource owners to offer computational power while retaining control and privacy. In Bakery, multiple contributors can participate in building AI models:

  • A contributor may supply a base model.
  • A third party could offer GPU resources from a remote location.

Now, let’s look into their latest research on ZKLoRA. In this research, the Bagel Research Team focuses on enabling efficient and secure verification of Low-Rank Adaptation (LoRA) updates for LLMs in distributed training environments. Traditionally, fine-tuning these models involves external contributors providing LoRA updates, but verifying that these updates are genuinely compatible with the base model while protecting proprietary parameters poses challenges.

Existing methods, such as rerunning a forward pass or manually inspecting large parameter sets, are computationally infeasible, especially for models with billions of parameters. Contributors’ proprietary LoRA weights must also be protected, while base model owners must verify the accuracy and validity of the updates. This creates a dual challenge: mAIntaining trust in decentralized and collaborative AI development while preserving intellectual property and computational efficiency. The lack of a robust and efficient verification mechanism for LoRA updates limits their scalability and secure use in real-world applications.

To address the challenge mentioned above, the Bagel Research Team introduced ZKLoRA. This zero-knowledge protocol combines cryptographic methods with fine-tuning techniques to ensure the secure verification of LoRA updates without exposing private weights. ZKLoRA employs zero-knowledge proofs, polynomial commitments, and succinct cryptographic designs to verify LoRA’s compatibility with base models efficiently. This innovation allows LoRA contributors to protect their intellectual property while enabling base model users to validate updates confidently.

The ZKLoRA protocol operates through a structured process. First, the base model user provides partial activations by running unaltered model layers. These partial activations are then used by the LoRA owner, who applies their proprietary updates and constructs a zero-knowledge proof. This proof ensures that the LoRA updates are valid and compatible with the base model without disclosing proprietary information. Verification, which takes just 1–2 seconds per module, ensures the integrity of each LoRA update, even for models with billions of parameters. For example, a 70-billion parameter model with 80 LoRA modules can be verified in only a few minutes. This efficiency makes ZKLoRA a scalable solution for conditions requiring frequent or large-scale compatibility checks.

Also, ZKLoRA was rigorously evaluated across various LLMs, including models like distilgpt2, Llama-3.3-70B, and Mixtral-8x7B. The researchers analyzed the total verification time, proof generation time, and settings time of the number of LoRA modules and their average parameter sizes. Results showed that even with higher LoRA counts, the increase in verification time was modest due to the succinct nature of ZKLoRA’s design. For instance, a model with 80 LoRA modules required less than 2 seconds per module for verification, while total proof generation and settings time, though dependent on module size, remained manageable. This demonstrates ZKLoRA’s capability to handle multi-adapter scenarios in large-scale deployments with minimal computational overhead.

The research highlights several key takeaways that underscore ZKLoRA’s impact:

  1. The protocol verifies LoRA modules in just 1–2 seconds, even for models with billions of parameters, ensuring real-time applicability.
  2. ZKLoRA scales efficiently with the number of LoRA modules, maintaining manageable proof generation and verification times.
  3. By integrating cryptographic techniques like zero-knowledge proofs and differential privacy, ZKLoRA ensures the security of proprietary LoRA updates and base models.
  4. The protocol enables trust-driven collaborations across geographically distributed teams without compromising data integrity or intellectual property.
  5. With minimal computational overhead, ZKLoRA is suitable for frequent compatibility checks, multi-adapter scenarios, and contract-based training pipelines.

In conclusion, Bagel has transformed decentralized AI development through its innovative platform, Bakery, and the ZKLoRA protocol. They have addressed critical challenges in fine-tuning LLMs, such as verifying LoRA updates securely and efficiently while preserving intellectual property. Bagel has also provided a robust framework for trust-driven collaboration. Bakery enables open-source contributors to monetize their work effectively. At the same time, ZKLoRA leverages advanced cryptographic techniques like zero-knowledge proofs and differential privacy to ensure secure and scalable compatibility checks. With verification times as short as 1–2 seconds per module, even for multi-billion parameter models, ZKLoRA demonstrates remarkable efficiency and makes it a practical solution for real-world applications. Finally, Bakery is the first product to utilize the Bagel model architecture. This architecture represents a core primitive that can be leveraged by future products developed by the Bagel team and other companies aiming to innovate in the open-source AI space.

Sources:

Thanks to the Bagel AI team for the thought leadership/ Resources for this article. Bagel AI team has supported us in this content/article.

The post Beyond Open Source AI: How Bagel’s Cryptographic Architecture, Bakery Platform, and ZKLoRA Drive Sustainable AI Monetization appeared first on MarkTechPost.

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