Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Exactly — controlling top-p is about setting the threshold properly so that the “little imp” behaves the way you want. ===
Exactly — controlling top-p is about setting the threshold properly so that the “little imp” behaves the way you want. Let’s go step by step.

==== - Top-p = cumulative probability threshold. ====
* The model sums the probabilities of tokens from highest to lowest, until the sum ≥ p.
* Only tokens included in this cumulative sum are eligible to be chosen.
* High top-p (close to 1.0, e.g., 0.95–0.99) - Includes many tokens → lots of variability - Little imp has more freedom → can pick unlikely paths → creative / hallucinating output
* Low top-p (e.g., 0.5–0.7) - Includes only the very strongest tokens → fewer choices → more deterministic - Little imp is restrained → less creativity

==== - Decide how conservative vs creative you want the model: - Deterministic / repeatable output → low top-p (0.5–0.7) - Moderate creativity → top-p 0.8–0.9 - Highly creative / exploratory → top-p 0.95+ ====
* Usually, top-p is tuned together with temperature: - Low temperature + low top-p → very safe, repeatable - High temperature + high top-p → very creative

==== - If your model is heavily quantized or you need consistent outputs, keep top-p moderate to low (0.6–0.8). ====
* Avoid setting top-p too high if your goal is deterministic reasoning or structured outputs (tables, summaries, etc.).

✅ Key insight

: 

If you want, I can make a visual showing how temperature, top-k, and top-p interact to pick tokens from competing neuron paths, which makes controlling top-p very intuitive.

Do you want me to do that?