cui-llama.rn/lib/typescript/index.d.ts

Fork of llama.rn for ChatterUI

import type { NativeContextParams, NativeLlamaContext, NativeCompletionParams, NativeCompletionTokenProb, NativeCompletionResult, NativeTokenizeResult, NativeEmbeddingResult, NativeSessionLoadResult, NativeCPUFeatures, NativeEmbeddingParams, NativeCompletionTokenProbItem, NativeCompletionResultTimings } from './NativeRNLlama';
import type { SchemaGrammarConverterPropOrder, SchemaGrammarConverterBuiltinRule } from './grammar';
import { SchemaGrammarConverter, convertJsonSchemaToGrammar } from './grammar';
import type { RNLlamaMessagePart, RNLlamaOAICompatibleMessage } from './chat';
export type { NativeContextParams, NativeLlamaContext, NativeCompletionParams, NativeCompletionTokenProb, NativeCompletionResult, NativeTokenizeResult, NativeEmbeddingResult, NativeSessionLoadResult, NativeEmbeddingParams, NativeCompletionTokenProbItem, NativeCompletionResultTimings, RNLlamaMessagePart, RNLlamaOAICompatibleMessage, SchemaGrammarConverterPropOrder, SchemaGrammarConverterBuiltinRule, };
export { SchemaGrammarConverter, convertJsonSchemaToGrammar };
export type TokenData = {
    token: string;
    completion_probabilities?: Array<NativeCompletionTokenProb>;
};
export declare enum GGML_TYPE {
    LM_GGML_TYPE_F32 = 0,
    LM_GGML_TYPE_F16 = 1,
    LM_GGML_TYPE_Q4_0 = 2,
    LM_GGML_TYPE_Q4_1 = 3,
    LM_GGML_TYPE_Q5_0 = 6,
    LM_GGML_TYPE_Q5_1 = 7,
    LM_GGML_TYPE_Q8_0 = 8,
    LM_GGML_TYPE_Q8_1 = 9,
    LM_GGML_TYPE_Q2_K = 10,
    LM_GGML_TYPE_Q3_K = 11,
    LM_GGML_TYPE_Q4_K = 12,
    LM_GGML_TYPE_Q5_K = 13,
    LM_GGML_TYPE_Q6_K = 14,
    LM_GGML_TYPE_Q8_K = 15,
    LM_GGML_TYPE_IQ2_XXS = 16,
    LM_GGML_TYPE_IQ2_XS = 17,
    LM_GGML_TYPE_IQ3_XXS = 18,
    LM_GGML_TYPE_IQ1_S = 19,
    LM_GGML_TYPE_IQ4_NL = 20,
    LM_GGML_TYPE_IQ3_S = 21,
    LM_GGML_TYPE_IQ2_S = 22,
    LM_GGML_TYPE_IQ4_XS = 23,
    LM_GGML_TYPE_I8 = 24,
    LM_GGML_TYPE_I16 = 25,
    LM_GGML_TYPE_I32 = 26,
    LM_GGML_TYPE_I64 = 27,
    LM_GGML_TYPE_F64 = 28,
    LM_GGML_TYPE_IQ1_M = 29,
    LM_GGML_TYPE_BF16 = 30,
    LM_GGML_TYPE_TQ1_0 = 34,
    LM_GGML_TYPE_TQ2_0 = 35,
    LM_GGML_TYPE_COUNT = 39
}
export type ContextParams = Omit<NativeContextParams, 'cache_type_k' | 'cache_type_v' | 'pooling_type'> & {
    cache_type_k?: GGML_TYPE;
    cache_type_v?: GGML_TYPE;
    pooling_type?: 'none' | 'mean' | 'cls' | 'last' | 'rank';
};
export type EmbeddingParams = NativeEmbeddingParams;
export type CompletionParams = Omit<NativeCompletionParams, 'emit_partial_completion' | 'prompt'> & {
    prompt?: string;
    messages?: RNLlamaOAICompatibleMessage[];
    chatTemplate?: string;
};
export type BenchResult = {
    modelDesc: string;
    modelSize: number;
    modelNParams: number;
    ppAvg: number;
    ppStd: number;
    tgAvg: number;
    tgStd: number;
};
export declare class LlamaContext {
    id: number;
    gpu: boolean;
    reasonNoGPU: string;
    model: {
        isChatTemplateSupported?: boolean;
    };
    constructor({ contextId, gpu, reasonNoGPU, model }: NativeLlamaContext);
    /**
     * Load cached prompt & completion state from a file.
     */
    loadSession(filepath: string): Promise<NativeSessionLoadResult>;
    /**
     * Save current cached prompt & completion state to a file.
     */
    saveSession(filepath: string, options?: {
        tokenSize: number;
    }): Promise<number>;
    getFormattedChat(messages: RNLlamaOAICompatibleMessage[], template?: string): Promise<string>;
    completion(params: CompletionParams, callback?: (data: TokenData) => void): Promise<NativeCompletionResult>;
    stopCompletion(): Promise<void>;
    tokenizeAsync(text: string): Promise<NativeTokenizeResult>;
    tokenizeSync(text: string): NativeTokenizeResult;
    detokenize(tokens: number[]): Promise<string>;
    embedding(text: string, params?: EmbeddingParams): Promise<NativeEmbeddingResult>;
    bench(pp: number, tg: number, pl: number, nr: number): Promise<BenchResult>;
    release(): Promise<void>;
}
export declare function getCpuFeatures(): Promise<NativeCPUFeatures>;
export declare function setContextLimit(limit: number): Promise<void>;
export declare function loadLlamaModelInfo(model: string): Promise<Object>;
export declare function initLlama({ model, is_model_asset: isModelAsset, pooling_type: poolingType, lora, ...rest }: ContextParams, onProgress?: (progress: number) => void): Promise<LlamaContext>;
export declare function releaseAllLlama(): Promise<void>;
//# sourceMappingURL=index.d.ts.map