新闻
Jev or a fine-tuned small model? We built a pipeline with both to see the real difference.
Distil Labs · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Distil Labs compared Jev, a zero-shot classifier, against fine-tuned small models on an accounts payable pipeline with three decision types.
- 为何重要
- Engineers building high-volume classification systems should care when deciding between hosted APIs, fine-tuned small models, or one-pass classifiers for specific tasks.
- 注意
- Results are synthetic data on one domain. Real-world performance depends heavily on task complexity, whether reasoning is needed, and whether structured output beyond labels is required.
- small model
- fine-tun
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- Agentic Context Engineering (Evolving Playbook)
- Differential Privacy Patterns
- Machine Learning Model-Based Routing
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