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Coinbase Fine-Tunes Qwen3.5-9B for Fraud
Brian Armstrong says Coinbase fine-tuned open-weight Qwen3.5-9B on internal data, outperforming frontier models on fraud prevention at lower cost.
Key points
- Brian Armstrong said Coinbase fine-tuned an open weight model, Qwen3.5-9B, on proprietary internal data for fraud prevention, and it outperformed frontier models at much lower cost and speed.
- He said Coinbase is making it easy to fine tune models internally for employees, with an internal harness skill invoked by the command "/fine-tune-and-serve-llm".
- He said he imagines some backend services could be replaced entirely by this new architecture.
- The original poster pointed to more technical details in a three-part blog post and linked to a page to join the future of agentic finance (AiFi).
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Original post
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).
I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.
We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:
"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"
I imagine we'll have some backend services that could be replaced entirely by this new architecture.
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From @brian_armstrong, @EpsilonPM, @FrankisTrying and others. Spam removed, with English and Chinese translations.
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