Inference
Min-P Sampling
A sampling method that keeps only tokens whose probability is at least a set fraction of the most likely token's probability, then samples from what remains.
In depth
A sampling method that keeps only tokens whose probability is at least a set fraction of the most likely token's probability, then samples from what remains. Because the cutoff scales with the top token, the filter is strict when the model is confident and loose when many tokens are plausible. Its authors showed it holds quality better than top-p at higher temperatures. It is supported in Hugging Face Transformers, vLLM, llama.cpp, and other open-source inference stacks.
Primary source: arxiv.org
Example
In practice, developers reach for Min-P Sampling when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Min-P Sampling?
A sampling method that keeps only tokens whose probability is at least a set fraction of the most likely token's probability, then samples from what remains.
Why does Min-P Sampling matter for AI developers?
Min-P Sampling sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Min-P Sampling?
Developers Digest publishes tutorials and videos that cover Inference topics including Min-P Sampling. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Mamba
Mamba is a sequence model architecture built on selective state space layers, where the SSM parameters depend on the current token so the model can choose what to remember or forget.
Inference
Multi-Token Prediction (MTP)
A training objective where a language model learns to predict the next several tokens at each position instead of only the next one, using extra output heads on a shared model trunk.
Inference
Top-K / Top-P Sampling
Two methods for controlling the randomness of model output during token generation.
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