ModelRefs / Min-P Sampling — AI Glossary
Min-P Sampling — AI Glossary
A sampling method filtering tokens below a fraction of the top token's probability, adapting dynamically to distribution sharpness.
Overview
Min-p (Nguyen et al. 2023) sets a dynamic cutoff: keep any token whose probability ≥ p × p_max. When the model is confident (peaked distribution), the effective k is small; when uncertain, more tokens survive. Outperforms top-p on coherence-diversity trade-offs; adopted by llama.cpp, Ollama, and several frontier APIs.
Reference details
| Topic | inference |
|---|---|
| Last reviewed | 2026-06-24 |
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Frequently asked questions
What is Min-P Sampling?
A sampling method filtering tokens below a fraction of the top token's probability, adapting dynamically to distribution sharpness.
What concepts are related to Min-P Sampling?
Closely related concepts include sampling, top p, temperature.