Transformers.Js

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transformers.js is an open-source library that enables state-of-the-art machine learning models to run directly in web browsers, eliminating the need for a server. It supports various AI tasks across NLP, computer vision, audio, and multimodal domains.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is transformers.js?

transformers.js is an open-source JavaScript library designed to bring state-of-the-art machine learning capabilities directly to the web browser. It allows developers to run Hugging Face's Transformer models without requiring a server, offering functional equivalence to the Python library. The tool supports a wide range of tasks including text classification, image segmentation, automatic speech recognition, and zero-shot object detection. It leverages ONNX Runtime for browser execution and allows for easy conversion of PyTorch, TensorFlow, or JAX models to ONNX using 🤗 Optimum. Developers can install it via NPM or use it directly in vanilla JS via CDN, with options to run models on CPU (WASM) or GPU (WebGPU) and adjust quantization for performance.

Best used for

Ideal for developers who need to integrate advanced machine learning models directly into web applications, enabling client-side inference without server dependencies. Especially valuable for building interactive AI experiences, reducing backend costs, and ensuring user data privacy by processing locally.

Common actions

run machine learning models

integrate AI into web

process data client-side

develop AI applications

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Capabilities

Key features

In-browser ML execution
No server required
Supports NLP, Vision, Audio
ONNX Runtime integration
WebGPU support
Model quantization options

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of machine learning tasks does transformers.js support?

transformers.js supports a wide array of tasks across different modalities, including Natural Language Processing (text classification, summarization), Computer Vision (image classification, object detection), Audio (automatic speech recognition, text-to-speech), and Multimodal tasks (embeddings, zero-shot classification). This broad support makes it versatile for various AI applications.

Can I use my own pre-trained models with transformers.js?

Yes, transformers.js is designed to work with models converted to ONNX format. You can easily convert your pre-trained PyTorch, TensorFlow, or JAX models to ONNX using 🤗 Optimum. This allows for flexibility in using custom or fine-tuned models within your web applications.

Does transformers.js support GPU acceleration in the browser?

Yes, transformers.js supports running models on the GPU via WebGPU, offering improved performance for computationally intensive tasks. By default, models run on the CPU (WASM), but you can specify 'webgpu' as the device option when allocating a pipeline. Note that WebGPU API is still experimental in some browsers.

How can I optimize model performance in resource-constrained environments?

To optimize performance and lower bandwidth in web browsers, it is advisable to use quantized versions of models. transformers.js allows you to adjust the 'dtype' option to select appropriate data types like 'fp16', 'q8' (default for WASM), or 'q4' for reduced model size and faster inference.

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