AI Sampling Parameter Demo

Temperature and top-p control how adventurous model output feels. This demo makes the math visible: you supply a small set of logits, the raw scores a model assigns to candidate next tokens, and the tool applies real softmax mathematics to turn them into probabilities at your chosen temperature, then applies top-p filtering to show which tokens survive. Low temperature sharpens the distribution toward the top token; high temperature flattens it toward randomness; top-p cuts the long tail once cumulative probability passes your threshold. It is labeled a simulation throughout, because no model runs here — only the sampling mathematics, which is the part worth understanding.

Quick Answer: Enter logits 2, 1, 0 at temperature 1 and top-p 0.9 and the tool returns probabilities about 66.5%, 24.5%, 9.0%, with the last token cut by top-p.

Free Online Tool

SIMULATION: see how temperature and top-p reshape a token distribution using real softmax math on logits you supply — not a live model.

Logits (comma separated, e.g. 2, 1, 0)

Temperature

Top-p

Educational simulation of the sampling math only. No model runs here, and no real tokens are generated.

Table of Contents

How to Use the AI Sampling Parameter Demo

Enter your input in the tool above — for example, logits 2, 1, 0 at temperature 1 and top-p 0.9.
Choose any options the tool offers, such as the conversion direction or the settings that fit your case.
Click the main button to run the tool and read the result it displays.
Check the result against what you expected, then adjust the input and run it again if needed.
Copy the output into your own document, code, or configuration wherever you need it.

Worked Example

Try it with logits 2, 1, 0 at temperature 1 and top-p 0.9: the tool returns probabilities about 66.5%, 24.5%, 9.0%, with the last token cut by top-p. That example is a good first test whenever you use the tool, because it confirms you entered the input in the expected format before you trust it with your real values. This tool is built for teaching and learning how sampling parameters shape model output, and the worked example shows the shape of a correct result so you can recognize a wrong one at a glance.

Key Features

Runs entirely in your browser — nothing you type is uploaded anywhere.
Built for teaching and learning how sampling parameters shape model output, with the result shown immediately and in full.
No account, no sign-up, and no software to install.
Clear error messages when an input is incomplete or in the wrong format.
Free to use as often as you like, for study, work, or checking homework.

Best Practices and Tips

Start with the worked example in the guide, then substitute your own values.
Double-check units and formats before trusting a result — most tool errors are input errors.
Keep a note of the exact inputs that produced a result you rely on, so you can reproduce it.
For anything important, cross-check with a second method or a known test value.
Bookmark the tool if you use it regularly, so the same workflow is always one click away.

Common Mistakes to Avoid

Assuming temperature changes what the model knows, rather than how it samples.
Setting top-p to 1 and temperature high, then wondering why output is incoherent.
Copying a result without checking it against a known example first.
Assuming the tool knows your context — it only sees the values you enter.

When This Tool Helps — and When It Doesn’t

This tool helps most when you need a fast, checkable answer for teaching and learning how sampling parameters shape model output and you can verify the result against a known example. It does not replace professional judgment: for decisions with real consequences, treat the output as one input among several, verify it independently, and remember that the tool can only work with exactly what you enter. Used that way — quick check, verified result, human decision — it saves time without creating false confidence.

Frequently Asked Questions

What does temperature actually change?

It rescales the logits before softmax. Values below 1 sharpen differences so the top token dominates; values above 1 flatten them so unlikely tokens get real chances. It changes sampling, not knowledge.

How does top-p differ from top-k?

Top-k keeps a fixed number of tokens. Top-p keeps however many tokens it takes to reach a cumulative probability mass, so the kept set grows or shrinks with the distribution shape.

Why is this called a simulation?

Because a real model computes logits from your text through billions of parameters. This demo skips that entirely and applies the same sampling math to logits you provide — honest about exactly which part is real.