In the news
Jev or a fine-tuned small model? We built a pipeline with both to see the real difference.
Distil Labs · Published · 3 min read
In 30 seconds
- What happened
- Distil Labs compared Jev, a zero-shot classifier, against fine-tuned small models on an accounts payable pipeline with three decision types.
- Why it matters
- Engineers building high-volume classification systems should care when deciding between hosted APIs, fine-tuned small models, or one-pass classifiers for specific tasks.
- Watch out
- 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
The patterns behind this
- Agentic Context Engineering (Evolving Playbook)
- Differential Privacy Patterns
- Machine Learning Model-Based Routing
Each one covers how the technique works, when it earns its cost, and where it breaks.
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