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Node.js module for the CLIP model.

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frost-beta/clip

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Node.js module for theCLIP model.

Powered bynode-mlx, a machinelearning framework for Node.js.

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import{coreasmx}from'@frost-beta/mlx';exporttypeImageInputType=Buffer|ArrayBuffer|string;exportinterfaceProcessedImage{data:Buffer;info:sharp.OutputInfo;}exportinterfaceClipInput{labels?:string[];images?:ProcessedImage[];}exportinterfaceClipOutput{labelEmbeddings?:mx.array;imageEmbeddings?:mx.array;}exportclassClip{constructor(modelDir:string);processImages(images:ImageInputType[]):Promise<ProcessedImage[]>;computeEmbeddings({ labels, images}:ClipInput):ClipOutput;/**     * Short hands of computeEmbeddings to convert results to JavaScript numbers     * and ensure the intermediate arrays are destroyed.     */computeLabelEmbeddingsJs(labels:string[]):number[][];computeImageEmbeddingsJs(images:ProcessedImage[]):number[][];/**     * Compute the cosine similarity between 2 embeddings.     */staticcomputeCosineSimilaritiy(a1:mx.array,a2:mx.array):mx.array;/**     * Compute the cosine similarities between 2 arrays of embeddings.     *     * A tuple will be returned, with the first element being the cosine     * similarity scores, and the second element being the indices sorted by     * their scores from larger to smalller.     */staticcomputeCosineSimilarities(x1:mx.array|number[][],x2:mx.array|number[][]):[mx.array,mx.array];}

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MIT

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