llm/
model_settings.rs

1use serde::{Deserialize, Serialize};
2
3#[derive(Debug, Clone, Default, PartialEq, Serialize, Deserialize, schemars::JsonSchema)]
4#[serde(rename_all = "camelCase", deny_unknown_fields)]
5pub struct ModelSettings {
6    /// Sampling temperature. Lower is more deterministic (e.g. `0` for grading).
7    #[serde(default, skip_serializing_if = "Option::is_none")]
8    pub temperature: Option<f32>,
9    /// Nucleus sampling: the probability mass to sample from.
10    #[serde(default, skip_serializing_if = "Option::is_none")]
11    pub top_p: Option<f32>,
12    /// Upper bound on the number of tokens generated in the response.
13    #[serde(default, skip_serializing_if = "Option::is_none")]
14    pub max_tokens: Option<u32>,
15}
16
17impl ModelSettings {
18    /// Returns `true` when no sampling controls are set.
19    pub fn is_empty(&self) -> bool {
20        self.temperature.is_none() && self.top_p.is_none() && self.max_tokens.is_none()
21    }
22}
23
24#[cfg(test)]
25mod tests {
26    use serde_json::from_str;
27
28    use super::*;
29
30    #[test]
31    fn json_round_trips_in_camel_case_and_rejects_unknown_fields() {
32        assert!(ModelSettings::default().is_empty());
33        assert_eq!(serde_json::to_value(ModelSettings::default()).unwrap(), serde_json::json!({}));
34
35        let settings = ModelSettings { temperature: Some(0.0), top_p: None, max_tokens: Some(1024) };
36        let json = serde_json::to_value(&settings).unwrap();
37        let parsed: ModelSettings =
38            serde_json::from_str(r#"{ "temperature": 0.2, "topP": 0.9, "maxTokens": 64 }"#).unwrap();
39
40        assert!(!settings.is_empty());
41        assert_eq!(json["temperature"], 0.0);
42        assert_eq!(json["maxTokens"], 1024);
43        assert!(json.get("topP").is_none());
44        assert_eq!(parsed, ModelSettings { temperature: Some(0.2), top_p: Some(0.9), max_tokens: Some(64) });
45        assert!(from_str::<ModelSettings>(r#"{ "frequencyPenalty": 1.0 }"#).is_err());
46    }
47}
llm/model_settings.rs

llm/
model_settings.rs
