Machine Learning¶

This component is an experimental machine learning local inference engine based on Transformers.js and the ONNX runtime.

You can use the component to leverage the inference runtime in the context of the browser. To try out some inference tasks, you can refer to the 1000+ models that are available in the Hugging Face Hub that are compatible with this runtime.

To enable the engine, flip the browser.ml.enable preference to true in about:config.

Running the pipeline API¶

You can use the Transformer.js pipeline API directly to perform inference, as long as the model is in our model hub.

The Transformers.js documentation provides a lot of examples that you can slightly adapt when running in Firefox.

In the example below, a text summarization task is performed using the summarization task:

const { createEngine } = ChromeUtils.importESModule("chrome://global/content/ml/EngineProcess.sys.mjs");
const options = {
  taskName: "summarization",
  modelId: "mozilla/text_summarization",
  modelRevision: "main"
  }
);

const engine = await createEngine(options);

const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' +
'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' +
'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' +
'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' +
'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' +
'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' +
'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' +
'tallest free-standing structure in France after the Millau Viaduct.';

const request = { args:  [text], options: { max_length: 100 } };
const res = await engine.run(request);
console.log(res[0]["summary_text"]);

When running this code, Firefox will look for models in the Mozilla model hub located at https://model-hub.mozilla.org which contains a curated list of models.

Available Options¶

Options passed to the createEngine function are verified and converted into a PipelineOptions object.

Below are the options available:

taskName: The name of the task the pipeline is configured for.
timeoutMS: The maximum amount of time in milliseconds the pipeline should wait for a response.
modelHubRootUrl: The root URL of the model hub where models are hosted.
modelHubUrlTemplate: A template URL for building the full URL for the model.
modelId: The identifier for the specific model to be used by the pipeline.
modelRevision: The revision for the specific model to be used by the pipeline.
tokenizerId: The identifier for the tokenizer associated with the model, used for pre-processing inputs.
tokenizerRevision: The revision for the tokenizer associated with the model, used for pre-processing inputs.
processorId: The identifier for any processor required by the model, used for additional input processing.
processorRevision: The revision for any processor required by the model, used for additional input processing.
logLevel: The log level used in the worker
runtimeFilename: Name of the runtime wasm file.

taskName and modelId are required, the others are optional and will be filled automatically using values pulled from Remote Settings when the task id is recognized.

Some values are also set from the preferences (set in about:config):

browser.ml.logLevel
browser.ml.modelHubRootUrl
browser.ml.modelHubUrlTemplate
browser.ml.modelCacheTimeout

Using the Hugging Face model hub¶

By default, the engine will use the Mozilla model hub and will error out if you try to use any other hub for security reasons.

If you want to use the Hugging Face model hub, you will need to run Firefox with the MOZ_ALLOW_EXTERNAL_ML_HUB environment variable set to 1, then set in about:config these two values:

browser.ml.modelHubRootUrl to https://huggingface.co
browser.ml.modelHubUrlTemplate to {model}/resolve/{revision}

The inference engine will then look for models in the Hugging Face model hub.

Running the internal APIs¶

Some inference tasks are doing more complex operations within the engine, such as image processing. For these tasks, you can use the internal APIs to run the inference. Those tasks are prefixed with moz.

In the example below, an image is converted to text using the moz-image-to-text task.

const { createEngine } = ChromeUtils.importESModule("chrome://global/content/ml/EngineProcess.sys.mjs");

// options needed for the task
const options = {taskName: "moz-image-to-text" };

// We create the engine object, using the options
const engine = await createEngine(options);

// Preparing a request
const request = {url: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg"};

// At this point we are ready to do some inference.
const res = await engine.run(request);

// The result is a string containing the text extracted from the image
console.log(res);

The following internal tasks are supported by the machine learning engine:

imageToText(request, model, tokenizer, processor)¶

Converts an image to text using a machine learning model.

Arguments:

request (object) – The request object containing image data.
request.url (string) – The URL of the image to process. If url is not provided, other fields are used.
request.data (ArrayBuffer) – The raw image data to process. Ignored if url is provided.
request.width (number) – The image width. Ignored if url is provided.
request.height (number) – The image height. Ignored if url is provided.
request.channels (number) – The image channels. Can be 1, 2, 3 or 4. Defaults to 4. Ignored if url is provided.
model (object) – The model used for inference.
tokenizer (object) – The tokenizer used for decoding.
processor (object) – The processor used for preparing image data.

Returns:

Promise.<object> – The result object containing the processed text.