siliconflow/huggingface.js

siliconflow/huggingface.js

Description: Use Hugging Face with JavaScript

License: MIT

Stars: 0

Forks: 0

Open issues: 0

Created: 2025-10-28T06:51:55Z

Pushed: 2025-10-28T07:30:09Z

Default branch: main

Fork: yes

Parent repository: huggingface/huggingface.js

Archived: no

README:

// Programmatically interact with the Hub

await createRepo({
repo: { type: "model", name: "my-user/nlp-model" },
accessToken: HF_TOKEN
});

await uploadFile({
repo: "my-user/nlp-model",
accessToken: HF_TOKEN,
// Can work with native File in browsers
file: {
path: "pytorch_model.bin",
content: new Blob(...)
}
});

// Use all supported Inference Providers!

await inference.chatCompletion({
model: "meta-llama/Llama-3.1-8B-Instruct",
provider: "sambanova", // or together, fal-ai, replicate, cohere …
messages: [
{
role: "user",
content: "Hello, nice to meet you!",
},
],
max_tokens: 512,
temperature: 0.5,
});

await inference.textToImage({
model: "black-forest-labs/FLUX.1-dev",
provider: "replicate",
inputs: "a picture of a green bird",
});

// and much more…

Hugging Face JS libraries

This is a collection of JS libraries to interact with the Hugging Face API, with TS types included.

• [@huggingface/inference](packages/inference/README.md): Use all supported (serverless) Inference Providers or switch to Inference Endpoints (dedicated) to make calls to 100,000+ Machine Learning models
• [@huggingface/hub](packages/hub/README.md): Interact with huggingface.co to create or delete repos and commit / download files
• [@huggingface/mcp-client](packages/mcp-client/README.md): A Model Context Protocol (MCP) client, and a tiny Agent library, built on top of InferenceClient.
• [@huggingface/gguf](packages/gguf/README.md): A GGUF parser that works on remotely hosted files.
• [@huggingface/dduf](packages/dduf/README.md): Similar package for DDUF (DDUF Diffusers Unified Format)
• [@huggingface/tasks](packages/tasks/README.md): The definition files and source-of-truth for the Hub's main primitives like pipeline tasks, model libraries, etc.
• [@huggingface/jinja](packages/jinja/README.md): A minimalistic JS implementation of the Jinja templating engine, to be used for ML chat templates.
• [@huggingface/space-header](packages/space-header/README.md): Use the Space mini_header outside Hugging Face
• [@huggingface/ollama-utils](packages/ollama-utils/README.md): Various utilities for maintaining Ollama compatibility with models on the Hugging Face Hub.
• [@huggingface/tiny-agents](packages/tiny-agents/README.md): A tiny, model-agnostic library for building AI agents that can use tools.

We use modern features to avoid polyfills and dependencies, so the libraries will only work on modern browsers / Node.js >= 18 / Bun / Deno.

The libraries are still very young, please help us by opening issues!

Installation

From NPM

To install via NPM, you can download the libraries as needed:

npm install @huggingface/inference
npm install @huggingface/hub
npm install @huggingface/mcp-client

Then import the libraries in your code:

import { InferenceClient } from "@huggingface/inference";
import { createRepo, commit, deleteRepo, listFiles } from "@huggingface/hub";
import { McpClient } from "@huggingface/mcp-client";
import type { RepoId } from "@huggingface/hub";

From CDN or Static hosting

You can run our packages with vanilla JS, without any bundler, by using a CDN or static hosting. Using ES modules, i.e. ``, you can import the libraries in your code:

import { InferenceClient } from 'https://cdn.jsdelivr.net/npm/@huggingface/inference@4.13.0/+esm';
import { createRepo, commit, deleteRepo, listFiles } from "https://cdn.jsdelivr.net/npm/@huggingface/hub@2.6.12/+esm";

Deno

// esm.sh
import { InferenceClient } from "https://esm.sh/@huggingface/inference"

import { createRepo, commit, deleteRepo, listFiles } from "https://esm.sh/@huggingface/hub"
// or npm:
import { InferenceClient } from "npm:@huggingface/inference"

import { createRepo, commit, deleteRepo, listFiles } from "npm:@huggingface/hub"

Usage examples

Get your HF access token in your account settings.

@huggingface/inference examples

import { InferenceClient } from "@huggingface/inference";

const HF_TOKEN = "hf_...";

const client = new InferenceClient(HF_TOKEN);

// Chat completion API
const out = await client.chatCompletion({
model: "meta-llama/Llama-3.1-8B-Instruct",
messages: [{ role: "user", content: "Hello, nice to meet you!" }],
max_tokens: 512
});
console.log(out.choices[0].message);

// Streaming chat completion API
for await (const chunk of client.chatCompletionStream({
model: "meta-llama/Llama-3.1-8B-Instruct",
messages: [{ role: "user", content: "Hello, nice to meet you!" }],
max_tokens: 512
})) {
console.log(chunk.choices[0].delta.content);
}

/// Using a third-party provider:
await client.chatCompletion({
model: "meta-llama/Llama-3.1-8B-Instruct",
messages: [{ role: "user", content: "Hello, nice to meet you!" }],
max_tokens: 512,
provider: "sambanova", // or together, fal-ai, replicate, cohere …
})

await client.textToImage({
model: "black-forest-labs/FLUX.1-dev",
inputs: "a picture of a green bird",
provider: "fal-ai",
})

// You can also omit "model" to use the recommended model for the task
await client.translation({
inputs: "My name is Wolfgang and I live in Amsterdam",
parameters: {
src_lang: "en",
tgt_lang: "fr",
},
});

// pass multimodal files or URLs as inputs
await client.imageToText({
model: 'nlpconnect/vit-gpt2-image-captioning',
data: await (await fetch('https://picsum.photos/300/300')).blob(),
})

// Using your own dedicated inference endpoint: https://hf.co/docs/inference-endpoints/
const gpt2Client = client.endpoint('https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2');
const { generated_text } = await gpt2Client.textGeneration({ inputs: 'The answer to the universe is' });

// Chat Completion
const llamaEndpoint = client.endpoint(
"https://router.huggingface.co/hf-inference/models/meta-llama/Llama-3.1-8B-Instruct"
);
const out = await llamaEndpoint.chatCompletion({
model: "meta-llama/Llama-3.1-8B-Instruct",
messages: [{ role: "user", content: "Hello, nice to meet you!" }],
max_tokens: 512,
});
console.log(out.choices[0].message);

@huggingface/hub examples

import { createRepo, uploadFile, deleteFiles } from…