Principle:Sgl project Sglang Sampling Parameters Preparation

Knowledge Sources	Nucleus Sampling SGLang
Domains	NLP, Text_Generation, Sampling
Last Updated	2026-02-10 00:00 GMT

Overview

A configuration pattern for specifying text generation sampling strategies including temperature, top-p, top-k, and other decoding parameters.

Description

Sampling parameters control the stochastic behavior of text generation. Temperature scales logits before softmax (higher = more random), top-p (nucleus sampling) truncates the probability distribution to the smallest set of tokens whose cumulative probability exceeds a threshold, and top-k limits candidates to the k highest-probability tokens. Additional parameters control penalties for repetition, early stopping conditions, and maximum output length. In SGLang, these are passed as a plain Python dictionary to the Engine.generate method.

Usage

Define sampling parameters whenever calling Engine.generate or the OpenAI-compatible API to control generation quality, diversity, and length. Use low temperature (0.0-0.3) for factual/deterministic outputs and higher values (0.7-1.0) for creative generation.

Theoretical Basis

Text generation sampling involves selecting the next token from a probability distribution:

$P(x_t|x_{<t})=\text{softmax}(\frac{z_t}{\tau })$

Where $\tau$ is the temperature parameter.

Top-p (Nucleus) sampling selects the smallest set $V_p$ such that: $\sum _{x\in V_p}P(x|x_{<t})\ge p$

Key parameters:

• temperature — Scaling factor for logits (0 = greedy, 1 = standard sampling)
• top_p — Nucleus sampling threshold (0.0-1.0)
• top_k — Maximum number of candidate tokens
• max_new_tokens — Hard limit on generated token count
• frequency_penalty / presence_penalty — Discourage repetition

Related Pages

Implemented By

• Implementation:Sgl_project_Sglang_Sampling_Parameters_Dict