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Coinbase微调Qwen3.5-9B反欺诈
Brian Armstrong称Coinbase用内部数据微调开源模型Qwen3.5-9B,在欺诈预防任务上以更低成本超越前沿模型。
要点
- Brian Armstrong 称 Coinbase 用专有内部数据微调了开放权重模型 Qwen3.5-9B,用于欺诈防范,在成本和时间上远低于前沿模型的情况下表现更好。
- 他表示 Coinbase 正让员工在公司内部轻松微调模型,内部 harness 中有一个技能,通过命令 "/fine-tune-and-serve-llm" 即可启动。
- 他表示设想一些后端服务可能会被这种新架构完全取代。
- 原推作者称更多技术细节见一篇三部分的博客文章,并附上加入 agentic finance(AiFi)未来的链接。
要点和反应摘要由 AI 依据本页推文整理,请以原推为准。 我们怎么用 AI
原推
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.
X 上的热门回复
X 上的反应: 回复整体认同专有数据才是真正的护城河,认为一个懂公司欺诈模式的 9B 模型胜过从未见过这些数据的强模型。有人质疑对比对象,指出拿来比的是 Opus 4.5,而现在已经到 5.5;还有人开玩笑说 Coinbase 的产品经理周五就会用这条命令写站会发言;也有人问开源的 Jev 变体是不是更适合这类任务的架构。
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