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

Bagel is a novel AI model architecture that revolutionizes open source AI development by enabling permissionless donations and ensuring donor revenue streams. Its design combines advanced cryptography and machine learning techniques to create a trustless, secure, and collaborative ecosystem. Their first platform, Bakery, is a unique AI fine-tuning and monetization platform built on the Bagel model architecture. It creates a collaborative environment 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 of ​​the Bagel came from its founder, Bidhan Roywho has a rich background in engineering and machine learning and has contributed to the world's largest ML infrastructure at Amazon Alexa, Cash App, and Instacart. Realizing the unsustainability of open source AI as a model for giving, Roy envisioned a system that would encourage donors to monetize their work. His introduction to cryptography during his work on Cash App's Bitcoin trading platform in 2017 became the basis for Bagel's innovative approach to combining cryptographic methods with AI development.

Bagel's unique value proposition is built around three key pillars:

1. Adjective: Bagel ensures that all structural or parametric contributions are verified using its novel ZKLoRA methodology, which provides a transparent trail of creative work and fosters accountability in collaborative AI development.
2. Ownership: Contributors maintain permanent claims to their innovations by using privacy-preserving containers and parameter obfuscation, which removes the need for formal licensing agreements while protecting intellectual property.
3. Privacy: Secure model encapsulation and layered obfuscation protect proprietary components, prevent unauthorized access even to untrusted or untrusted computer environments, ensuring privacy and trust throughout the development process.

Bagel's Core Innovations

• Unauthorized Contributions: Bagel allows developers, researchers, and resource owners to contribute to the development of an AI model without requiring express permissions or prior agreements. This method of decentralization eliminates barriers to entry.
• Net worth: A unique feature of Bagel is its ability to include and distribute revenue to all ecosystem contributors accordingly. The platform accurately tracks contributions and model upgrades using cryptographic techniques, ensuring that contributors are rewarded equally.
• Cryptography Meets Machine Learning: Bagel's innovative architecture relies on a combination of cryptographic techniques and machine learning advances, including:
  • Parameter-Efficient Fine Tuning (PEFT): It improves model tuning processes, reducing resource requirements while maintaining performance.
  • ZKLoRA: An innovation of the Bagel Research Team – a knowledge-free protocol that ensures LoRA model compatibility updates without disclosing proprietary data, ensuring safe and efficient interactions.

Bagel's architecture is implemented through its own platform, Bakery. It enables decentralized AI development by allowing developers to contribute models and configurations securely, dataset providers to share proprietary data privately using cryptographic methods, and resource owners to enable integration while maintaining control and privacy. In Bakery, many contributors can participate in building AI models:

• The donor may provide a basic model.
• A third party can provide GPU services remotely.

Now, let's look at their latest research on ZKLoRA. In this study, the Bagel Research Team focused on enabling efficient and secure validation of Low-Rank Adaptation (LoRA) updates for LLMs in distributed training environments. Traditionally, fine-tuning these models involves external contributors providing LoRA updates, but ensuring that these updates are truly consistent with the underlying model while protecting proprietary parameters poses challenges.

Existing methods, such as restarting the forward pass or manually testing large parameter sets, are mathematically infeasible, especially for models with billions of parameters. The proprietary weights of LoRA contributors must also be protected, while the owners of the base models must ensure the accuracy and validity of the updates. This creates a two-fold challenge: Maintaining trust in decentralized AI development and collaboration 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 aforementioned challenge, the Bagel Research Team has launched ZKLoRA. This zero-information protocol combines cryptographic methods and fine-tuning techniques to ensure secure verification of LoRA updates without disclosing secret weights. ZKLoRA uses proof of ignorance, polynomial commitment, and short cryptographic designs to ensure LoRA's compatibility with basic models effectively. This innovation allows LoRA contributors to protect their intellectual property while allowing users of the underlying model to verify updates with confidence.

The ZKLoRA protocol works in a systematic way. First, the basic model user provides partial activation by using unmodified model layers. This is used in part by the owner of LoRA, which uses its own identity updates and creates a proof of zero. This proof ensures that LoRA updates are valid and consistent with the base model without disclosing proprietary information. Validation, which takes only 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 just a few minutes. This efficiency makes ZKLoRA a scalable solution for situations requiring frequent or large-scale parallel testing.

Also, ZKLoRA was rigorously tested across various LLMs, including models such as distilgpt2, Llama-3.3-70B, and Mixtral-8x7B. The researchers analyzed the total verification time, proof generation time, and configuration time for a number of LoRA modules and their average parameter sizes. The results showed that even with higher LoRA calculations, the increase in verification time was still small due to the short nature of the ZKLoRA design. For example, a model with 80 LoRA modules required 2 seconds per module for verification, while the generation of complete proofs and settings time, although dependent on the module size, remained manageable. This demonstrates ZKLoRA's ability to handle multiple adapter instances in large-scale applications with minimal computational overhead.

The study highlights several key takeaways that underline the impact of ZKLoRA:

1. The protocol validates LoRA modules in just 1-2 seconds, even models with billions of parameters, ensuring real-time performance.
2. ZKLoRA scales well with the number of LoRA modules, keeping proof generation and validation times manageable.
3. By combining cryptographic techniques such as zero-knowledge proofs and discrete privacy, ZKLoRA ensures the security of LoRA's proprietary updates and underlying models.
4. The protocol enables trust-driven collaboration across geographically distributed teams without compromising the integrity of data 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 revolutionized the development of augmented AI through its new platform, Bakery, and the ZKLoRA protocol. They have faced significant challenges in optimizing LLMs, such as ensuring LoRA updates safely and efficiently while preserving intellectual property. Bagel also provided a strong framework for trust-driven collaboration. Bakery allows open source contributors to successfully monetize their work. At the same time, ZKLoRA uses advanced cryptographic techniques such as zero-knowledge proofs and unique privacy to ensure safe and scalable compatibility testing. With validation times as short as 1–2 seconds per module, even for multi-billion parameter models, ZKLoRA exhibits remarkable efficiency and makes it a viable solution for real-world applications. Finally, Bakery is the first product to use the Bagel model architecture. This architecture represents a prototype that may be used in 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. The Bagel AI team supported us on this content/article.