Transformers.js

The Transformers.js provider runs ONNX models locally in Node.js using Transformers.js v4. It supports CPU inference and a WebGPU backend; no external inference API is required.

Installation

Transformers.js and its ONNX runtimes are not included in the default install. Install the runtime alongside Promptfoo in your project:

npm install promptfoo @huggingface/transformers@^4.0.0

For a global installation, use npm install -g promptfoo @huggingface/transformers@^4.0.0.

For a one-off eval, run npx --package=promptfoo --package=@huggingface/transformers@^4.0.0 promptfoo eval -c /absolute/path/to/promptfooconfig.yaml from an empty directory outside an existing npm project, with neither package installed locally. If either package is already installed in your project, use the project installation command above. Model files are downloaded separately on first use.

Quick Start

Embeddings

providers:
  - transformers:feature-extraction:Xenova/all-MiniLM-L6-v2

Popular models: Xenova/all-MiniLM-L6-v2 (384d), onnx-community/all-MiniLM-L6-v2-ONNX (384d), Xenova/bge-small-en-v1.5 (384d), nomic-ai/nomic-embed-text-v1.5 (768d)

Text Generation

providers:
  - transformers:text-generation:Xenova/gpt2

Popular models: Xenova/gpt2, onnx-community/Qwen3-0.6B-ONNX, onnx-community/Llama-3.2-1B-Instruct-ONNX

note

Text generation runs on CPU and is best for testing. For production, consider Ollama or cloud APIs.

Configuration

Common Options

These options apply to both embedding and text generation providers:

Option	Description	Default
device	'auto', 'cpu', 'gpu', 'wasm', 'webgpu', 'cuda', 'dml', 'coreml', 'webnn', 'webnn-npu', 'webnn-gpu', 'webnn-cpu'	'auto'
dtype	Quantization: 'fp32', 'fp16', 'q8', 'int8', 'uint8', 'q4', 'bnb4', 'q4f16'	'auto'
cacheDir	Override model cache directory	System default
localFilesOnly	Skip downloads, use cached models only	false
revision	Model version/branch	'main'
sessionOptions	ONNX runtime session options, passed through as session_options	-

Embedding Options

providers:
  - id: transformers:feature-extraction:Xenova/bge-small-en-v1.5
    config:
      prefix: 'Represent this sentence for searching relevant passages: ' # BGE retrieval queries
      pooling: cls # BGE v1.5 uses the CLS token embedding
      normalize: true # L2 normalize embeddings
      dtype: q8

Model prefixes: Follow the model card for your embedding model. BGE v1.5 uses the instruction above for retrieval queries; documents need no prefix. E5 v2 uses prefix: 'query: ' for queries and prefix: 'passage: ' for documents. MiniLM models need no prefix.

Nomic Embed v1.5 requires prefix: 'search_query: ' for retrieval queries and prefix: 'search_document: ' for indexed documents. Use the model card's task prefix for other workloads. Keep query and document preprocessing compatible with your vector index; rebuild affected stored embeddings when changing document preprocessing, model, or dimensions.

tip

transformers:embeddings:<model> is an alias for transformers:feature-extraction:<model>.

Text Generation Options

providers:
  - id: transformers:text-generation:onnx-community/Qwen3-0.6B-ONNX
    config:
      maxNewTokens: 256
      temperature: 0.7
      topK: 50
      topP: 0.9
      doSample: true
      repetitionPenalty: 1.1
      noRepeatNgramSize: 3
      numBeams: 1
      returnFullText: false
      dtype: q4

Using for Similarity Assertions

Use local embeddings as a grading provider for similar assertions:

defaultTest:
  options:
    provider:
      embedding:
        id: transformers:feature-extraction:Xenova/all-MiniLM-L6-v2

providers:
  - openai:gpt-4o-mini

tests:
  - vars:
      question: 'What is photosynthesis?'
    assert:
      - type: similar
        value: 'Photosynthesis converts light to chemical energy in plants'
        threshold: 0.8

Or override per-assertion:

assert:
  - type: similar
    value: 'Expected output'
    threshold: 0.75
    provider: transformers:feature-extraction:Xenova/all-MiniLM-L6-v2

Performance

• Caching: Pipelines are cached after first load. Initial model download may take time, but subsequent runs are fast.
• Quantization: Use dtype: q4 or dtype: q8 for faster inference and lower memory. Use dtype: q4f16 for WebGPU-optimized quantization.
• WebGPU: v4 includes a WebGPU runtime written in C++ with improved performance. Use device: webgpu on supported systems.
• Concurrency: For limited RAM, use promptfoo eval -j 1 to run serially.

Troubleshooting

Problem	Solution
Dependency not installed	Follow the installation instructions to install the runtime alongside Promptfoo.
Model not found	Verify model exists at HuggingFace with ONNX weights. Try Xenova or onnx-community models.
Out of memory	Use dtype: q4, run with -j 1, or try smaller models
Slow first run	Models download on first use. Pre-download with await pipeline('feature-extraction', 'model-name')

Supported Models

Browse compatible models at huggingface.co/models?library=transformers.js.

Key organizations: onnx-community (optimized ONNX exports, recommended for v4), Xenova (legacy ONNX models, still compatible)