- We propose OPML (Optimistic Machine Learning), which enables AI model inference and training/fine-tuning on the blockchain system using optimistic approach (OPML is also called **FPML**, where "FP" refers to fraud proof).
- OPML can provide ML service with low cost and high efficiency compared to ZKML. The participation requirement for OPML is low: We are now able to run OPML with a large language model, e.g., 7B-LLaMA (the model size is around 26GB) on a common PC without GPU.
- OPML adopts a verification game (similar to Truebit and Optimistic Rollup systems) to guarantee decentralized and verifiable consensus on the ML service. 
  - The requester first initiates an ML service task.
  - The server then finishes the ML service task and commits results on chain.
  - The verifier will validate the results. Suppose there exists a verifier who declares the results are wrong. It starts a verification game with verification game (dispute game) with the server and tries to disprove the claim by pinpointing one concrete erroneous step.
  - Finally, arbitration about a single step will be conducted on smart contract.
- OPML is still under development, and is open-sourced: [OPML-Labs/opml: OPML: OPtimistic Machine Learning on Blockchain (github.com)](https://github.com/OPML-Labs/opml) 
