transformers-js

Topics in the source

• Transformers.js - Machine Learning for JavaScript
• When to Use This Skill
• Installation
• NPM Installation
• Browser Usage (CDN)
• Core Concepts
• 1. Pipeline API
• 2. Model Selection

Transformers.js enables running state-of-the-art machine learning models directly in JavaScript across browsers and server-side runtimes (Node.js, Bun, Deno), with no Python server required. Use this skill when you need to: Run ML models for text analysis, generation, or translation in JavaScript Perform image classification, object detection, or segmentation Implement speech recognition or audio processing Build multimodal AI applications (text-to-image, image-to-text, etc.) Run models client-side in the browser without a backend The pipeline API is the easiest way to use models. It groups together preprocessing, model inference, and postprocessing: ⚠️ Memory Management: All pipelines must be disposed with pipe.dispose() when finished to prevent memory leaks. See examples in Code Examples for cleanup patterns across different environments. You can specify a custom model as the second argument: Finding Models: Browse available Transformers.js models on Hugging Face Hub: All models: https://huggingface.co/models?library=transformers.js&sort=trending By task: Add pipelinetag parameter Text generation: https://huggingface.co/models?pipelinetag=text-generation&library=transformers.js&s

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