Struct SamplingCategory 

pub struct SamplingCategory {
    pub temperature: f32,
    pub top_p: f32,
    pub top_k: u32,
    pub presence_penalty: f32,
    pub frequency_penalty: f32,
    pub repetition_penalty: f32,
    pub dry_multiplier: f32,
    pub dry_base: f32,
    pub dry_allowed_length: u32,
    pub lz_penalty: f32,
    pub min_p: Option<f32>,
    pub top_n_sigma: Option<f32>,
}

Per-category sampling defaults from MODEL.toml.

Fields

temperature: f32

top_p: f32

top_k: u32

presence_penalty: f32

frequency_penalty: f32

repetition_penalty: f32

Multiplicative penalty on already-seen tokens (1.0 = disabled). Populated from MODEL.toml [sampling.*].repetition_penalty via build.rs.

dry_multiplier: f32

DRY (Don’t-Repeat-Yourself) sampler parameters. Penalises tokens that extend repeated n-grams past dry_allowed_length with an exponential dry_multiplier * dry_base^(match_len - allowed) — the targeted fix for phrase-level attractors (e.g. the ```bash cd … cargo test ``` fence-narration loop observed in Qwen3.5-35B-A3B-FP8 opencode sessions at turn ≥ 8).

presence_penalty on its own is a FLAT per-unique-token hit (does not scale with repetition count), so it can’t break a phrase attractor where individual tokens already paid their penalty once. DRY scales with the repeat-length and is the published remedy (oobabooga/text-generation-webui#5677, used in llama.cpp / Aphrodite / TabbyAPI).

dry_multiplier = 0.0 disables DRY for this category (default for every preset unless MODEL.toml sets it explicitly).

dry_base: f32

dry_allowed_length: u32

lz_penalty: f32

LZ penalty (arXiv:2504.20131). Per-extension n-gram penalty over a 256-token rolling window. Frequency-weighted and length- scaled, so it correctly distinguishes “phrase loop” from “legitimate vocabulary reuse” without the flat-per-token presence_penalty regression. 0.0 = disabled. SGLang reference strength = 0.2 (lossless on AIME/GPQA).

min_p: Option<f32>

Model-declared min-p, or None when MODEL.toml is silent.

Option, not f32, because absence and 0.0 mean opposite things here. The server ships --default-min-p 0.08 and every request that does not name min_p takes it, so a model whose card specifies min_p = 0 had no way to say so: [behavior].min_p_floor only ever RAISES min_p (min_p.max(floor)), and the preset did not carry the value at all. A plain f32 defaulting to 0.0 would silently strip the 0.08 floor from every model that has a [sampling.*] table, which is the opposite regression.

Some(x) outranks the CLI default and is outranked by generation_config.json — the same precedence temperature/top_k/top_p already follow. None preserves the CLI-owned behaviour exactly.

top_n_sigma: Option<f32>

Model-declared top-n-sigma, or None when MODEL.toml is silent.

Same absence-vs-zero problem as min_p: the server ships --default-top-n-sigma 1.0, so a model whose card asks for NO sigma filter had no way to say so. Some(0.0) disables it; None leaves the CLI default owning the field.

Trait Implementations

impl Clone for SamplingCategory

fn clone(&self) -> SamplingCategory

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SamplingCategory

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Copy for SamplingCategory