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August 11 – 20, 2025
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OPML: Build an efficient on-chain AI framework that transcends ZKML with innovative solutions
OPML: A Machine Learning Framework Based on Optimistic Methods
We have proposed a new framework called OPML(Optimistic Machine Learning ), which can efficiently execute AI model inference and training on blockchain systems. Compared to ZKML, OPML has lower costs and higher efficiency. OPML's hardware requirements are very low, and a regular PC can run OPML tasks that include large language models such as 7B-LLaMA without a GPU.
OPML uses a verification game mechanism to ensure the decentralization and verifiability of ML services. The process is as follows:
Single-Stage Verification Game
The key points of a single-stage OPML include:
In basic testing, we can complete DNN inference within 2 seconds, and the entire challenge process can be completed within 2 minutes.
Multi-Stage Verification Game
To overcome the limitations of a single-stage solution, we propose a multi-stage verification game:
Taking the LLaMA model as an example, we adopt a two-stage OPML method:
The multi-stage method can achieve α times computational acceleration compared to the single-stage method, while significantly reducing the size of the Merkle tree.
Consistency and Determinism
To ensure the consistency of ML results, we have taken the following measures:
These technologies effectively overcome the challenges posed by floating-point variables and platform differences, enhancing the reliability of OPML calculations.
OPML is still under continuous development. We welcome individuals interested in this project to join and contribute to the development of OPML.