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…Read original on X
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Coinbase says a fine-tuned open model beats frontier systems
The exchange got better fraud detection at lower cost from a small open-weight model on proprietary data, a pattern more enterprises can copy.
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Coinbase says a fine-tuned open model beats frontier systems
Coinbase says a fine-tuned open model beats frontier models
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