LLM Parameter Playground - Simulate Temperature, Top-P & Penalties
Tuning LLM API calls requires more than just good prompt copywriting; it requires mastering generation hyper-parameters. What exactly does Temperature=0.2 do mathematically versus Temperature=1.2? How does Top-P truncate the cumulative probability tail? This educational simulator visualizes how next-token softmax probability curves shift in real time as you adjust sampling parameters.
When to Use This Tool
- Deterministic vs Creative Tuning: Configure optimal temperature for deterministic JSON/coding tasks (0.0 - 0.2) vs creative marketing copy (0.7 - 1.0).
- Repetition Loop Prevention: Tune Frequency and Presence penalties to prevent infinite loop chatter in open-ended chat agents.
- Nucleus Sampling (Top-P) Mastery: Understand why combining high temperature with low Top-P maintains coherence while introducing novelty.
- AI Engineering Education: Visually explain LLM probabilistic generation mechanics to junior developers and product managers.
How to Use
- Inspect Candidate Tokens: View candidate next tokens (e.g. "developer", "engineer", "designer", "scientist") and raw logits.
- Adjust Temperature: Slide Temperature from 0.0 to 2.0 to see the softmax probability distribution flatten or sharpen.
- Tune Top-P & Top-K: Slide Top-P to see cutoff thresholds dynamically eliminate improbable tail tokens.
- Observe Sampling Effects: Review the simulated choice selection highlighting likely vs forbidden tokens.
Input & Output Example
Input:
Base Tokens: ["code" (45%), "software" (30%), "system" (15%), "banana" (1%)]
Output:
Temp=0.1: "code" becomes 99.8%
Temp=1.5: "banana" probability jumps from 1% to 14% (Hallucination danger)
Frequently Asked Questions (FAQ)
Q. Should I adjust both Temperature and Top-P at the same time?
A. OpenAI officially advises adjusting either Temperature OR Top-P, not both simultaneously, because their effects overlap. Tuning one while keeping the other at 1.0 provides far more predictable control.
Q. What does Temperature=0 actually mean?
A. Mathematically, Temperature=0 acts as an argmax function (greedy decoding). The model deterministically selects the single token with the highest logit score 100% of the time.
Q. What is the difference between Frequency and Presence penalty?
A. Presence penalty penalizes a token once if it has appeared anywhere in the text (encouraging bringing up new subjects). Frequency penalty penalizes a token proportionally to how many times it has already appeared (discouraging phrase repetition).
Engineering & Privacy Notes
- Implements authentic Softmax with temperature scaling formulas used across modern deep learning runtimes.
- Runs purely in browser client-side canvas/SVG without external network calls.
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