Today, we’re excited to open-source OwLLM v1.0 (colloquially called “Owl LM”), the first Open-source, web3-native Large Language Model. OwLLM has been trained on millions of transactions, has more than 100 million parameters, and is designed to be chain-agnostic.
Early LLM models, like ChatGPT, have been explored for web3 applications; however, their general-purpose nature may not be the best fit for a wide swath of addressable applications in web3 such as transaction analysis, threat detection, payment analytics, and more. To help devs harness the full potential of LLMs, we built an entirely new foundational model from scratch, one that was specifically designed to analyze and understand blockchain transactions.
In its current release, OwLLM has already proved itself capable of identifying MEV transactions with a 94% accuracy rate. As we continue to evolve and refine OwLLM—hopefully with the help of the community—we hope it will unlock new applications, such as conversational blockchain explorers, in-dApp MEV protection, (smarter) smart-contract security audits, malicious transaction monitoring, new regulatory compliance tools, emergent systematic/programmatic trading strategies, and more.
We're making OwLLM v1 open-source today to encourage developers to use and improve this model. To inspect the source code or start using OwLLM for your project, visit the repo here.
If you’re interested in exploring OwLLM for your project, would like to contribute novel data or insights, or have practical use cases for OwLLM, we'd love to hear from you: send an email to email@example.com. Join us in refining and expanding OwLLM!