Verifying LoRA fine-tuning updates for large language models without exposing sensitive parameters has been a longstanding challenge in AI development. Traditional methods are slow, inefficient, and risky for protecting proprietary data.
Enter ZKLoRA, a zero-knowledge proof protocol that enables secure and efficient verification of LoRA updates. By compiling LoRA-augmented layers into cryptographic circuits, ZKLoRA ensures compatibility between private LoRA modules and base models in just 1–2 seconds per module—even for multi-billion parameter LLMs like GPT2 and LLaMA.
Key Features:
Privacy-Preserving: Keeps proprietary LoRA weights private during verification.
Efficiency: Scales with minimal overhead, even for decentralized workflows.
Open Source: Explore and implement the protocol in your projects.
Benchmarks show impressive scalability, making this a potential game-changer for secure collaboration in AI pipelines, particularly for decentralized and open-source ecosystems.
The open-source repo is live, and we’d love to hear your thoughts.
The paradox of open source AI is that while the code is open, the economics are closed. The current system collapses by 2025:
Training competitive models already costs $500K+
Value flows only to centralized platforms
Independent researchers can't compete at scale
Community contributions lack economic feedback loops
We're Bagel. Using cryptography to rebuild the foundation:
Private training (FHE)
Fair attribution
Decentralized infrastructure
Early proof: 26,000+ ML engineers + enterprise teams building production AI while maintaining full sovereignty.
The Bakery is live: https://bakery.bagel.net
Looking for ML researchers and engineers who want to fix this with us.
If you're looking to get in early on a free tool that allows you to finetune your model using encrypted data sets you can sign up here: https://waitlist.bagel.net/
Enter ZKLoRA, a zero-knowledge proof protocol that enables secure and efficient verification of LoRA updates. By compiling LoRA-augmented layers into cryptographic circuits, ZKLoRA ensures compatibility between private LoRA modules and base models in just 1–2 seconds per module—even for multi-billion parameter LLMs like GPT2 and LLaMA.
Key Features:
Privacy-Preserving: Keeps proprietary LoRA weights private during verification. Efficiency: Scales with minimal overhead, even for decentralized workflows. Open Source: Explore and implement the protocol in your projects. Benchmarks show impressive scalability, making this a potential game-changer for secure collaboration in AI pipelines, particularly for decentralized and open-source ecosystems.
The open-source repo is live, and we’d love to hear your thoughts.