Beyond Open Source AI: How Bagel's Crypto Architecture, Bakery Platform, and ZKLoRA Drive Sustainable AI Monetization

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 collaborative, secure and trustless ecosystem. Your first platform, Bakeryis a unique ai model tuning and monetization platform built on Bagel model architecture. It creates a collaborative space where developers can tune ai models without compromising the privacy of their proprietary resources or exposing sensitive model parameters.

Origin and Vision

the idea for Bagel arose from its founder, Bidhan Roywho has extensive engineering and machine learning experience and has contributed to the world's largest machine learning 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 crypto during his work at Cash App's bitcoin trading platform in 2017 became the foundation of BagelThe innovative approach of combining cryptographic methods with ai development.

BagelThe unique value proposition of is based on three fundamental 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. Property: Contributors retain perpetual rights to 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 computing environments, ensuring privacy and trust throughout the development process.

Bagel's Top Innovations

• Contributions without permission: Bagel It allows developers, researchers, and resource owners to contribute to ai model development without requiring explicit permissions or prior agreements. This decentralized approach removes barriers to entry.
• Income attribution: BagelThe unique feature of is its ability to fairly attribute and distribute income to all contributors in the ecosystem. The platform accurately tracks contributions and model improvements using cryptographic techniques, ensuring that contributors are rewarded proportionately.
• crypto meets machine learning: BagelThe innovative architecture is based on a fusion of cryptographic methods and advances in machine learning, including:
  • Parameter Efficient Fine Tuning (PEFT): Optimizes model tuning processes, reducing resource requirements while maintaining performance.
  • GLORY: The latest innovation from the Bagel Research Team: a zero-knowledge protocol that checks LoRA updates for base model compatibility without exposing proprietary data, ensuring secure and efficient collaboration.

BagelThe architecture is implemented through its platform, Bakery. Enables the development of decentralized ai 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 maintaining control and privacy . In BakerySeveral contributors can participate in creating ai models:

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

Now, let's discuss their latest research on ZKLoRA. In this research, the Bagel The research team focuses on enabling efficient and secure verification of Low Rank Adaptation (LoRA) updates for LLM in distributed training environments. Traditionally, tuning these models involves external contributors providing LoRA updates, but verifying that these updates are actually compatible with the base model while protecting proprietary parameters poses challenges.

Existing methods, such as rerunning a direct pass or manually inspecting large sets of parameters, 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 updates. This creates a double 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 its scalability and secure use in real-world applications.

To address the challenge mentioned above, the Bagel Presentation of the research team ZKLORA. This zero-knowledge protocol combines cryptographic methods with tuning techniques to ensure 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 allowing users of the base model to validate updates with confidence.

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 test ensures that LoRA updates are valid and compatible with the base model without revealing proprietary information. Verification, which takes only 1 to 2 seconds per module, ensures the integrity of every LoRA update, even for models with billions of parameters. For example, a 70 billion parameter model with 80 LoRA modules can be verified in just a few minutes. This efficiency makes ZKLoRA a scalable solution for conditions that require frequent or large-scale compatibility checks.

Furthermore, ZKLoRA was rigorously evaluated on several LLMs, including models such as distilgpt2, Llama-3.3-70B, and Mixtral-8x7B. The researchers analyzed the total verification time, test generation time, and configuration time for the number of LoRA modules and their average parameter sizes. The results showed that even with higher LoRA counts, the increase in verification time was modest due to the concise nature of the ZKLoRA design. For example, a model with 80 LoRA modules required less than 2 seconds per module for verification, while the total test generation and configuration time, although dependent on module size, was still manageable. This demonstrates ZKLoRA's ability to handle multiple adapter scenarios in large-scale deployments with minimal computational overhead.

The research highlights several key findings that underline the impact of ZKLoRA:

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 test generation and verification times.
3. By integrating cryptographic techniques such as zero-knowledge proofs and differential privacy, ZKLoRA ensures the security of LoRA's proprietary base models and updates.
4. The protocol enables trust-based collaborations between 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 the development of decentralized ai through its innovative platform. Bakeryand the ZKLoRA protocol. They have addressed critical challenges in tuning LLMs, such as verifying LoRA updates securely and efficiently while preserving intellectual property. Bagel It has also provided a strong framework for trust-based collaboration. Bakery allows open source contributors to monetize their work effectively. At the same time, ZKLoRA leverages advanced cryptographic techniques such as zero-knowledge proofs and differential privacy to ensure secure and scalable compatibility checks. With verification times as low as 1 to 2 seconds per module, even for multimillion-dollar parameter models, ZKLoRA demonstrates remarkable efficiency and makes it a practical solution for real-world applications. Finally, Bakery It is the first product that uses the Bagel model architecture. This architecture represents a primitive core that can be used by future products developed by the company. Bagel team and other companies looking to innovate in the open source ai space.

Sources:

Thanks to the Bagel ai team for the thought leadership and resources for this article. The Bagel ai team has supported us in this content/article.