SamplingOptions

dynamo_llm::protocols::common

Struct SamplingOptions 

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pub struct SamplingOptions {
Show 14 fields
    pub n: Option<u8>,
    pub best_of: Option<u8>,
    pub presence_penalty: Option<f32>,
    pub frequency_penalty: Option<f32>,
    pub repetition_penalty: Option<f32>,
    pub temperature: Option<f32>,
    pub top_p: Option<f32>,
    pub top_k: Option<i32>,
    pub min_p: Option<f32>,
    pub use_beam_search: Option<bool>,
    pub length_penalty: Option<f32>,
    pub seed: Option<i64>,
    pub include_stop_str_in_output: Option<bool>,
    pub guided_decoding: Option<GuidedDecodingOptions>,

}
Expand description

Collection of options that control the sampling behavior of the inference engine.

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§n: Option<u8>

Number of output sequences to return for the given prompt

§best_of: Option<u8>

Number of output sequences that are generated from the prompt. From these best_of sequences, the top n sequences are returned. best_of must be greater than or equal to n. This is treated as the beam width when use_beam_search is True. By default, best_of is set to n.

§presence_penalty: Option<f32>

Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.

§frequency_penalty: Option<f32>

Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.

§repetition_penalty: Option<f32>

Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. Values > 1 encourage the model to use new tokens, while values < 1 encourage the model to repeat tokens.

§temperature: Option<f32>

Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.

§top_p: Option<f32>

Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.

§top_k: Option<i32>

Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens.

§min_p: Option<f32>

Float that represents the minimum probability for a token to be considered, relative to the probability of the most likely token. Must be in [0, 1]. Set to 0 to disable this.

§use_beam_search: Option<bool>

Whether to use beam search instead of sampling.

§length_penalty: Option<f32>

Float that penalizes sequences based on their length. Used in beam search.

§seed: Option<i64>

The seed to use when sampling

§include_stop_str_in_output: Option<bool>

Whether to include the stop string in the output.

§guided_decoding: Option<GuidedDecodingOptions>

Guided Decoding Options

Implementations§

Trait Implementations§

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impl Clone for SamplingOptions

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fn clone(&self) -> SamplingOptions

Returns a duplicate of the value. Read more
1.0.0 · Source§

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

Performs copy-assignment from source. Read more
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impl Debug for SamplingOptions

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

Formats the value using the given formatter. Read more
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impl Default for SamplingOptions

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fn default() -> SamplingOptions

Returns the “default value” for a type. Read more
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impl<'de> Deserialize<'de> for SamplingOptions

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fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>
where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer. Read more
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impl Serialize for SamplingOptions

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fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>
where __S: Serializer,

Serialize this value into the given Serde serializer. Read more

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