@huggingface/transformers
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State-of-the-art Machine Learning for the web. Run đŸ¤— Transformers directly in your browser, with no need for a server!
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TypeScript
export type DynamicCache = Record<string, Tensor> & _DynamicCache;
/**
* @typedef {Record<string, Tensor> & _DynamicCache} DynamicCache
*/
export const DynamicCache: new (entries?: Record<string, Tensor>) => DynamicCache;
import { Tensor } from './utils/tensor.js';
/**
* A cache class that stores past key values as named tensors.
*/
declare class _DynamicCache {
/**
* Create a DynamicCache, optionally pre-populated with entries.
* @param {Record<string, Tensor>} [entries] Initial name→Tensor mappings.
*/
constructor(entries?: Record<string, Tensor>);
/**
* Get the cached sequence length. This requires at least one attention cache entry to be present.
* @returns {number} The past sequence length.
*/
get_seq_length(): number;
/**
* Update the cache in-place with new entries, disposing replaced GPU tensors.
* @param {Record<string, Tensor>} newEntries The new name → Tensor mappings.
*/
update(newEntries: Record<string, Tensor>): void;
/**
* Dispose all contained tensors whose data resides on the GPU.
* Returns a promise that resolves when all disposals are complete.
* @returns {Promise<void>} Promise that resolves when all GPU tensors are disposed.
*/
dispose(): Promise<void>;
}
export {};
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