transformers-js

Transformers.js - Machine Learning for JavaScript

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.

When to Use This Skill

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

Installation

NPM Installation

npm install @huggingface/transformers

Browser Usage (CDN)

<script type="module">
  import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers';
</script>

Core Concepts

1. Pipeline API

The pipeline API is the easiest way to use models. It groups together preprocessing, model inference, and postprocessing:

import { pipeline } from '@huggingface/transformers'

⚠️ 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.

2. Model Selection

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 pipeline_tag parameter
  • Text generation: https://huggingface.co/models?pipeline_tag=text-generation&library=transformers.js&sort=trending
  • Image classification: https://huggingface.co/models?pipeline_tag=image-classification&library=transformers.js&sort=trending
  • Speech recognition: https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&library=transformers.js&sort=trending

Tip: Filter by task type, sort by trending/downloads, and check model cards for performance metrics and usage examples.

3. Device Selection

Choose where to run the model:

4. Quantization Options

Control model precision vs. performance:

Supported Tasks

Note: All examples below show basic usage.

Natural Language Processing

Text Classification

Named Entity Recognition (NER)

Question Answering

Text Generation

For streaming and chat: See Text Generation Guide for:

• Streaming token-by-token output with TextStreamer
• Chat/conversation format with system/user/assistant roles
• Generation parameters (temperature, top_k, top_p)
• Browser and Node.js examples
• React components and API endpoints

Translation

Summarization

Zero-Shot Classification

Computer Vision

Image Classification

Object Detection

Image Segmentation

Depth Estimation

Zero-Shot Image Classification

Audio Processing

Automatic Speech Recognition

Audio Classification

Text-to-Speech

Multimodal

Image-to-Text (Image Captioning)

Document Question Answering

Zero-Shot Object Detection

Feature Extraction (Embeddings)

Finding and Choosing Models

Browsing the Hugging Face Hub

Discover compatible Transformers.js models on Hugging Face Hub:

Base URL (all models):

Filter by task using the pipeline_tag parameter:

Sort options:

• &sort=trending - Most popular recently
• &sort=downloads - Most downloaded overall
• &sort=likes - Most liked by community
• &sort=modified - Recently updated

Choosing the Right Model

Consider these factors when selecting a model:

1. Model Size

• Small (< 100MB): Fast, suitable for browsers, limited accuracy
• Medium (100MB - 500MB): Balanced performance, good for most use cases
• Large (> 500MB): High accuracy, slower, better for Node.js or powerful devices

2. Quantization Models are often available in different quantization levels:

• fp32 - Full precision (largest, most accurate)
• fp16 - Half precision (smaller, still accurate)
• q8 - 8-bit quantized (much smaller, slight accuracy loss)
• q4 - 4-bit quantized (smallest, noticeable accuracy loss)

3. Task Compatibility Check the model card for:

• Supported tasks (some models support multiple tasks)
• Input/output formats
• Language support (multilingual vs. English-only)
• License restrictions

4. Performance Metrics Model cards typically show:

• Accuracy scores
• Benchmark results
• Inference speed
• Memory requirements

Example: Finding a Text Generation Model

Tips for Model Selection

1. Start Small: Test with a smaller model first, then upgrade if needed
2. Check ONNX Support: Ensure the model has ONNX files (look for onnx folder in model repo)
3. Read Model Cards: Model cards contain usage examples, limitations, and benchmarks
4. Test Locally: Benchmark inference speed and memory usage in your environment
5. Filter by Library: Use library=transformers.js to find compatible models: https://huggingface.co/models?library=transformers.js
6. Version Pin: Use specific git commits in production for stability:

   const pipe = await pipeline('task', 'model-id', { revision: 'abc123' });

Advanced Configuration

Environment Configuration (env)

The env object provides comprehensive control over Transformers.js execution, caching, and model loading.

Quick Overview:

import { env, LogLevel } from '@huggingface/transformers';
 
// View version
console.log(env.version); // e.g., '4.x'
 
// Common settings
env.allowRemoteModels = true;  // Load from Hugging Face Hub
env.allowLocalModels = false;  // Load from file system
env.localModelPath = '/models/'; // Local model directory
env.useFSCache = true;         // Cache models on disk (Node.js)
env.useBrowserCache = true;    // Cache models in browser
env.cacheDir = './.cache';     // Cache directory location
// Optional: override logging level (default is LogLevel.WARNING)
env.logLevel = LogLevel.INFO;
 
// Optional: custom fetch for auth headers, retries, abort signals, etc.
env.fetch = (url, options) =>
  fetch(url, {
    ...options,
    headers: {
      ...options?.headers,
      Authorization: `Bearer ${HF_TOKEN}`,
    },
  });

Configuration Patterns:

// Development: Fast iteration with remote models
env.allowRemoteModels = true;
env.useFSCache = true;
 
// Production: Local models only
env.allowRemoteModels = false;
env.allowLocalModels = true;
env.localModelPath = '/app/models/';
 
// Custom CDN
env.remoteHost = 'https://cdn.example.com/models';
 
// Disable caching (testing)
env.useFSCache = false;
env.useBrowserCache = false;

For complete documentation on all configuration options, caching strategies, cache management, pre-downloading models, and more, see:

→ Configuration Reference

ModelRegistry (v4)

ModelRegistry gives you visibility and control over model assets before loading a pipeline. Use it to estimate download size, check cache status, inspect available dtypes, and clear cached artifacts for a specific task/model/options tuple.

import { ModelRegistry } from '@huggingface/transformers';
 
const task = 'feature-extraction';
const modelId = 'onnx-community/all-MiniLM-L6-v2-ONNX';
const modelOptions = { dtype: 'fp32' };
 
// List required files for this pipeline
const files = await ModelRegistry.get_pipeline_files(task, modelId, modelOptions);
 
// Check if assets are already cached
const cached = await ModelRegistry.is_pipeline_cached(task, modelId, modelOptions);
 
// Inspect precision formats available for this model
const dtypes = await ModelRegistry.get_available_dtypes(modelId);
 
console.log({ files: files.length, cached, dtypes });

For production patterns and full API coverage, see ModelRegistry Reference.

Standalone Tokenization (@huggingface/tokenizers)

For tokenization-only workflows, use @huggingface/tokenizers. It is a separate lightweight package useful when you need fast tokenization/encoding without loading full model inference pipelines.

npm install @huggingface/tokenizers

import { Tokenizer } from '@huggingface/tokenizers';

Working with Tensors

import { AutoTokenizer, AutoModel } from '@huggingface/transformers';
 
// Load tokenizer and model separately for more control
const tokenizer = await AutoTokenizer.from_pretrained('bert-base-uncased');
const model = await AutoModel.from_pretrained('bert-base-uncased');
 
// Tokenize input
const inputs = await tokenizer('Hello world!');
 
// Run model
const outputs = await model(inputs);

Batch Processing

const classifier = await pipeline('sentiment-analysis');
 
// Process multiple texts
const results = await classifier([
  'I love this!',
  'This is terrible.',
  'It was okay.'
]);

Runtime-Specific Considerations

WebGPU Usage

WebGPU provides GPU acceleration in browsers and server-side runtimes (when supported):

const pipe = await pipeline('text-generation', 'onnx-community/gemma-3-270m-it-ONNX', {
  device: 'webgpu',
  dtype: 'fp32'
});

Note: Use webgpu when available and fall back to WASM/CPU when not supported in the current runtime.

WASM Performance

WASM is the most compatible execution backend across runtimes:

// Optimized for browsers with quantization
const pipe = await pipeline('sentiment-analysis', 'model-id', {
  dtype: 'q8'  // or 'q4' for even smaller size
});

Progress Tracking & Loading Indicators

Models can be large (ranging from a few MB to several GB) and consist of multiple files. Track download progress by passing a callback to the pipeline() function:

import { pipeline } from '@huggingface/transformers';
 
// Track progress for each file
const fileProgress = {};
 
function onProgress(info) {
  if (info.status === 'progress_total') {
    console.log(`Total: ${info.progress.toFixed(1)}%`);
    return;
  }
 
  console.log(`${info.status}: ${info.file ?? ''}`);
  
  if (info.status === 'progress') {
    fileProgress[info.file] = info.progress;
    console.log(`${info.file}: ${info.progress.toFixed(1)}%`);
  }
  
  if (info.status === 'done') {
    console.log(`✓ ${info.file} complete`);
  }
}
 
// Pass callback to pipeline
const classifier = await pipeline('sentiment-analysis', null, {
  progress_callback: onProgress
});

Progress Info Properties:

interface ProgressInfo {
  status: 'initiate' | 'download' | 'progress' | 'progress_total' | 'done' | 'ready';
  name: string;      // Model id or path
  file?: string;     // File being processed (per-file events)
  progress?: number; // Percentage (0-100, for 'progress' and 'progress_total')
  loaded?: number;   // Bytes downloaded (only for 'progress' status)
  total?: number;    // Total bytes (only for 'progress' status)
}

For complete examples including browser UIs, React components, CLI progress bars, and retry logic, see:

→ Pipeline Options - Progress Callback

Error Handling

try {
  const pipe = await pipeline('sentiment-analysis', 'model-id');
  const result = await pipe('text to analyze');
} catch (error) {
  if (error.message.includes('fetch')) {
    console.error('Model download failed. Check internet connection.');
  } else if (error.message.includes('ONNX')) {
    console.error('Model execution failed. Check model compatibility.');
  } else {
    console.error('Unknown error:', error);
  }
}

Performance Tips

1. Reuse Pipelines: Create pipeline once, reuse for multiple inferences
2. Use Quantization: Start with q8 or q4 for faster inference
3. Batch Processing: Process multiple inputs together when possible
4. Cache Models: Models are cached automatically (see Caching Reference for details on browser Cache API, Node.js filesystem cache, and custom implementations)
5. WebGPU for Large Models: Use WebGPU for models that benefit from GPU acceleration
6. Prune Context: For text generation, limit max_new_tokens to avoid memory issues
7. Clean Up Resources: Call pipe.dispose() when done to free memory

Memory Management

IMPORTANT: Always call pipe.dispose() when finished to prevent memory leaks.

const pipe = await pipeline('sentiment-analysis');
const result = await pipe('Great product!');
await pipe.dispose();  // ✓ Free memory (100MB - several GB per model)

When to dispose:

• Application shutdown or component unmount
• Before loading a different model
• After batch processing in long-running apps

Models consume significant memory and hold GPU/CPU resources. Disposal is critical for browser memory limits and server stability.

For detailed patterns (React cleanup, servers, browser), see Code Examples

Troubleshooting

Model Not Found

• Verify model exists on Hugging Face Hub
• Check model name spelling
• Ensure model has ONNX files (look for onnx folder in model repo)

Memory Issues

• Use smaller models or quantized versions (dtype: 'q4')
• Reduce batch size
• Limit sequence length with max_length

WebGPU Errors

• Check browser compatibility (Chrome 113+, Edge 113+)
• Try dtype: 'fp16' if fp32 fails
• Fall back to WASM if WebGPU unavailable

Reference Documentation

This Skill

• Pipeline Options - Configure pipeline() with progress_callback, device, dtype, etc.
• Configuration Reference - Global env configuration for caching and model loading
• ModelRegistry Reference - Inspect files, cache status, dtypes, and clear cache before loading pipelines
• Caching Reference - Browser Cache API, Node.js filesystem cache, and custom cache implementations
• Text Generation Guide - Streaming, chat format, and generation parameters
• Model Architectures - Supported models and selection tips
• Code Examples - Real-world implementations for different runtimes

Official Transformers.js

• Official docs: https://huggingface.co/docs/transformers.js
• API reference: https://huggingface.co/docs/transformers.js/api/pipelines
• Model hub: https://huggingface.co/models?library=transformers.js
• GitHub: https://github.com/huggingface/transformers.js
• Examples: https://github.com/huggingface/transformers.js-examples

Best Practices

1. Always Dispose Pipelines: Call pipe.dispose() when done - critical for preventing memory leaks
2. Start with Pipelines: Use the pipeline API unless you need fine-grained control
3. Test Locally First: Test models with small inputs before deploying
4. Monitor Model Sizes: Be aware of model download sizes for web applications
5. Handle Loading States: Show progress indicators for better UX
6. Version Pin: Pin specific model versions for production stability
7. Error Boundaries: Always wrap pipeline calls in try-catch blocks
8. Progressive Enhancement: Provide fallbacks for unsupported browsers
9. Reuse Models: Load once, use many times - don't recreate pipelines unnecessarily
10. Graceful Shutdown: Dispose models on SIGTERM/SIGINT in servers

Quick Reference: Task IDs

This skill enables you to integrate state-of-the-art machine learning capabilities directly into JavaScript applications without requiring separate ML servers or Python environments.