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@huggingface/inference

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Typescript client for the Hugging Face Inference Providers and Inference Endpoints

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/** * See the registered mapping of HF model ID => Nebius model ID here: * * https://huggingface.co/api/partners/nebius/models * * This is a publicly available mapping. * * If you want to try to run inference for a new model locally before it's registered on huggingface.co, * you can add it to the dictionary "HARDCODED_MODEL_ID_MAPPING" in consts.ts, for dev purposes. * * - If you work at Nebius and want to update this mapping, please use the model mapping API we provide on huggingface.co * - If you're a community member and want to add a new supported HF model to Nebius, please open an issue on the present repo * and we will tag Nebius team members. * * Thanks! */ import type { FeatureExtractionOutput, TextGenerationOutput } from "@huggingface/tasks"; import type { BodyParams } from "../types.js"; import { omit } from "../utils/omit.js"; import { BaseConversationalTask, BaseTextGenerationTask, TaskProviderHelper, type FeatureExtractionTaskHelper, type TextToImageTaskHelper, } from "./providerHelper.js"; import { InferenceClientProviderOutputError } from "../errors.js"; import type { ChatCompletionInput } from "../../../tasks/dist/commonjs/index.js"; const NEBIUS_API_BASE_URL = "https://api.studio.nebius.ai"; interface NebiusBase64ImageGeneration { data: Array<{ b64_json: string; }>; } interface NebiusEmbeddingsResponse { data: Array<{ embedding: number[]; }>; } interface NebiusTextGenerationOutput extends Omit<TextGenerationOutput, "choices"> { choices: Array<{ text: string; }>; } export class NebiusConversationalTask extends BaseConversationalTask { constructor() { super("nebius", NEBIUS_API_BASE_URL); } override preparePayload(params: BodyParams<ChatCompletionInput>): Record<string, unknown> { const payload = super.preparePayload(params) as Record<string, unknown>; const responseFormat = params.args.response_format; if (responseFormat?.type === "json_schema" && responseFormat.json_schema?.schema) { payload["guided_json"] = responseFormat.json_schema.schema; } return payload; } } export class NebiusTextGenerationTask extends BaseTextGenerationTask { constructor() { super("nebius", NEBIUS_API_BASE_URL); } override preparePayload(params: BodyParams): Record<string, unknown> { return { ...params.args, model: params.model, prompt: params.args.inputs, }; } override async getResponse(response: NebiusTextGenerationOutput): Promise<TextGenerationOutput> { if ( typeof response === "object" && "choices" in response && Array.isArray(response?.choices) && response.choices.length > 0 && typeof response.choices[0]?.text === "string" ) { return { generated_text: response.choices[0].text, }; } throw new InferenceClientProviderOutputError("Received malformed response from Nebius text generation API"); } } export class NebiusTextToImageTask extends TaskProviderHelper implements TextToImageTaskHelper { constructor() { super("nebius", NEBIUS_API_BASE_URL); } preparePayload(params: BodyParams): Record<string, unknown> { return { ...omit(params.args, ["inputs", "parameters"]), ...(params.args.parameters as Record<string, unknown>), response_format: "b64_json", prompt: params.args.inputs, model: params.model, }; } makeRoute(): string { return "v1/images/generations"; } async getResponse( response: NebiusBase64ImageGeneration, url?: string, headers?: HeadersInit, outputType?: "url" | "blob" ): Promise<string | Blob> { if ( typeof response === "object" && "data" in response && Array.isArray(response.data) && response.data.length > 0 && "b64_json" in response.data[0] && typeof response.data[0].b64_json === "string" ) { const base64Data = response.data[0].b64_json; if (outputType === "url") { return `data:image/jpeg;base64,${base64Data}`; } return fetch(`data:image/jpeg;base64,${base64Data}`).then((res) => res.blob()); } throw new InferenceClientProviderOutputError("Received malformed response from Nebius text-to-image API"); } } export class NebiusFeatureExtractionTask extends TaskProviderHelper implements FeatureExtractionTaskHelper { constructor() { super("nebius", NEBIUS_API_BASE_URL); } preparePayload(params: BodyParams): Record<string, unknown> { return { input: params.args.inputs, model: params.model, }; } makeRoute(): string { return "v1/embeddings"; } async getResponse(response: NebiusEmbeddingsResponse): Promise<FeatureExtractionOutput> { return response.data.map((item) => item.embedding); } }