LlamaModel

ruvector_sparse_inference::model::runners

Struct LlamaModel 

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pub struct LlamaModel {
    pub metadata: ModelMetadata,
    pub layers: Vec<LlamaLayer>,
    pub embed_tokens: Embedding,
    pub norm: RMSNorm,
    pub lm_head: Option<Linear>,
}
Expand description

Llama model for sparse inference

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§metadata: ModelMetadata§layers: Vec<LlamaLayer>§embed_tokens: Embedding§norm: RMSNorm§lm_head: Option<Linear>

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impl ModelRunner for LlamaModel

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fn forward( &self, input: &ModelInput, config: &InferenceConfig, ) -> Result<ModelOutput, SparseInferenceError>

Forward pass with optional sparse computation
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fn get_predictor(&self, layer_idx: usize) -> Option<&LowRankPredictor>

Get predictor for a specific layer (if available)
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fn calibrate( &mut self, samples: &[ModelInput], ) -> Result<CalibrationStats, SparseInferenceError>

Calibrate predictors with sample data
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fn metadata(&self) -> &ModelMetadata

Get model metadata

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