```ts
// Programatically interact with the Hub
await createRepo({
repo: {type: "model", name: "my-user/nlp-model"},
credentials: {accessToken: HF_TOKEN}
});
await uploadFile({
repo: "my-user/nlp-model",
credentials: {accessToken: HF_TOKEN},
// Can work with native File in browsers
file: {
path: "pytorch_model.bin",
content: new Blob(...)
}
});
// Use hosted inference
await inference.translation({
model: 't5-base',
inputs: 'My name is Wolfgang and I live in Berlin'
})
await inference.textToImage({
model: 'stabilityai/stable-diffusion-2',
inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
parameters: {
negative_prompt: 'blurry',
}
})
// 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 Inference Endpoints (dedicated) and Inference API (serverless) 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/agents](packages/agents/README.md): Interact with HF models through a natural language interface
- [@huggingface/gguf](packages/gguf/README.md): A GGUF parser that works on remotely hosted files.
- [@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/space-header](packages/space-header/README.md): Use the Space `mini_header` outside Hugging Face
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:
```bash
npm install @huggingface/inference
npm install @huggingface/hub
npm install @huggingface/agents
```
Then import the libraries in your code:
```ts
import { HfInference } from "@huggingface/inference";
import { HfAgent } from "@huggingface/agents";
import { createRepo, commit, deleteRepo, listFiles } from "@huggingface/hub";
import type { RepoId, Credentials } 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](https://hacks.mozilla.org/2018/03/es-modules-a-cartoon-deep-dive/), i.e. `
```
### Deno
```ts
// esm.sh
import { HfInference } from "https://esm.sh/@huggingface/inference"
import { HfAgent } from "https://esm.sh/@huggingface/agents";
import { createRepo, commit, deleteRepo, listFiles } from "https://esm.sh/@huggingface/hub"
// or npm:
import { HfInference } from "npm:@huggingface/inference"
import { HfAgent } from "npm:@huggingface/agents";
import { createRepo, commit, deleteRepo, listFiles } from "npm:@huggingface/hub"
```
## Usage examples
Get your HF access token in your [account settings](https://huggingface.co/settings/tokens).
### @huggingface/inference examples
```ts
import { HfInference } from "@huggingface/inference";
const HF_TOKEN = "hf_...";
const inference = new HfInference(HF_TOKEN);
// Chat completion API
const out = await inference.chatCompletion({
model: "mistralai/Mistral-7B-Instruct-v0.2",
messages: [{ role: "user", content: "Complete the this sentence with words one plus one is equal " }],
max_tokens: 100
});
console.log(out.choices[0].message);
// Streaming chat completion API
for await (const chunk of inference.chatCompletionStream({
model: "mistralai/Mistral-7B-Instruct-v0.2",
messages: [{ role: "user", content: "Complete the this sentence with words one plus one is equal " }],
max_tokens: 100
})) {
console.log(chunk.choices[0].delta.content);
}
// You can also omit "model" to use the recommended model for the task
await inference.translation({
model: 't5-base',
inputs: 'My name is Wolfgang and I live in Amsterdam'
})
await inference.textToImage({
model: 'stabilityai/stable-diffusion-2',
inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
parameters: {
negative_prompt: 'blurry',
}
})
await inference.imageToText({
data: await (await fetch('https://picsum.photos/300/300')).blob(),
model: 'nlpconnect/vit-gpt2-image-captioning',
})
// Using your own dedicated inference endpoint: https://hf.co/docs/inference-endpoints/
const gpt2 = inference.endpoint('https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2');
const { generated_text } = await gpt2.textGeneration({inputs: 'The answer to the universe is'});
//Chat Completion
const mistal = inference.endpoint(
"https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
);
const out = await mistal.chatCompletion({
model: "mistralai/Mistral-7B-Instruct-v0.2",
messages: [{ role: "user", content: "Complete the this sentence with words one plus one is equal " }],
max_tokens: 100,
});
console.log(out.choices[0].message);
```
### @huggingface/hub examples
```ts
import { createRepo, uploadFile, deleteFiles } from "@huggingface/hub";
const HF_TOKEN = "hf_...";
await createRepo({
repo: "my-user/nlp-model", // or {type: "model", name: "my-user/nlp-test"},
credentials: {accessToken: HF_TOKEN}
});
await uploadFile({
repo: "my-user/nlp-model",
credentials: {accessToken: HF_TOKEN},
// Can work with native File in browsers
file: {
path: "pytorch_model.bin",
content: new Blob(...)
}
});
await deleteFiles({
repo: {type: "space", name: "my-user/my-space"}, // or "spaces/my-user/my-space"
credentials: {accessToken: HF_TOKEN},
paths: ["README.md", ".gitattributes"]
});
```
### @huggingface/agents example
```ts
import {HfAgent, LLMFromHub, defaultTools} from '@huggingface/agents';
const HF_TOKEN = "hf_...";
const agent = new HfAgent(
HF_TOKEN,
LLMFromHub(HF_TOKEN),
[...defaultTools]
);
// you can generate the code, inspect it and then run it
const code = await agent.generateCode("Draw a picture of a cat wearing a top hat. Then caption the picture and read it out loud.");
console.log(code);
const messages = await agent.evaluateCode(code)
console.log(messages); // contains the data
// or you can run the code directly, however you can't check that the code is safe to execute this way, use at your own risk.
const messages = await agent.run("Draw a picture of a cat wearing a top hat. Then caption the picture and read it out loud.")
console.log(messages);
```
There are more features of course, check each library's README!
## Formatting & testing
```console
sudo corepack enable
pnpm install
pnpm -r format:check
pnpm -r lint:check
pnpm -r test
```
## Building
```
pnpm -r build
```
This will generate ESM and CJS javascript files in `packages/*/dist`, eg `packages/inference/dist/index.mjs`.
