APIs

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_new_tokens: 100 } };
const res = await engine.run(request);
console.log(res[0]["summary_text"]);

The code sample above executes the LLM and returns the complete output after the computation is finished. Alternatively, you can receive the output incrementally by using the asynchronous generator method runWithGenerator provided by the engine.

let summaryText = "";
for await (const chunk of engine.runWithGenerator(request)){
   summaryText += chunk.text;
}

You can use the browser console or toolbox to run this example. To enable the browser console, flip the following option in about:config: devtools.chrome.enabled. To get access to the full toolbox, set the devtools.debugger.remote-enabled option. We recommend using the toolbox to get access to more tools. You will get a security warning when starting it, when the toolbox connects to the browser.

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.

  • featureId: The identifier for the feature to be used by the pipeline.

  • engineId: The identifier for the engine to be used by the pipeline.

  • timeoutMS: The maximum amount of time in milliseconds the worker will run (-1 to never expire).

  • modelHub: The model hub to use, can be huggingface or mozilla. When used, modelHubRootUrl and modelHubUrlTemplate are ignored.

  • 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.

  • dtype: quantization level, can be fp32, fp16, q8, int8, uint8, q4, bnb4, q4f16`. Defaults to q8

  • device: device to use (wasm or gpu). Defaults to wasm

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.

To learn about the different inference tasks, refer to this Hugging Face documentation: Tasks

featureId is used to uniquely identify the feature that will be used by the pipeline and store corresponding options in Remote Settings – See the ml-inference-options collection.

engineId is used to manage the lifecycle of the engine. When not provided, it defaults to default-engine. Everytime a new engine is created using createEngine, the API will ensure that there’s a single engine with the given id. If the options of the existing engine are not different, the instance is reused. If they differ, the engine is reinitialized with the new options. This ensures we don’t have too many engines running at once since it takes a lot of resources. To make sure your engine is not destroyed or reused elsewhere, set that value with a unique id that matches your component.

When an engine is created, an inference process is created if it’s not already there, and a new worker is launched for that engine. The inference process is unique and shared by all engines.

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

  • browser.ml.logLevel: Set to “All” to see all logs, which are useful for debugging.

  • browser.ml.modelHubRootUrl: Model hub root URL used to download models

  • browser.ml.modelHubUrlTemplate: Model URL template

  • browser.ml.modelCacheTimeout: Worker timeout in ms. Default value used for timeoutMS

  • browser.ml.modelCacheMaxSize: Maximum disk size for ML model cache (in GiB)

URL allow and deny list

We keep a Remote Settings collection called ml-model-allow-deny-list that contains URL prefixes that are allowed or denied.

Each record comes with the following fields:

  • urlPrefix: The URL prefix to allow or deny

  • filter: Set to ALLOW to allow, DENY to deny

  • description: an optional description

When the API is about to fetch a file, its URL is controlled in the allow/deny list.

Examples of patterns:

Each URL is tested and needs to be included in the allowlist and not in the denylist

To bypass this check and allow Firefox to download any file for runnings models, you need to use the MOZ_ALLOW_EXTERNAL_ML_HUB environment variable.

If you want to add a new hub, organization or a specific model, ask us by opening a ticket.

Using the Hugging Face model hub

By default, the engine will use the Mozilla model hub. You will need to pass huggingface as modelHub.

The inference engine will then look for models in the Hugging Face model hub. If the URL is not allowed (see previous section) and you still want to experiment with the model, use MOZ_ALLOW_EXTERNAL_ML_HUB.

To run against a Hugging Face model, visit this page and select on the top left corner tasks. You can pick a task and then choose a model.

For example, models for the summarization tasks compatible with our inference engine are listed here.

Let’s say you want to pick the Xenova/distilbart-cnn-6-6 model. All you have to do is use the id when calling our createEngine pipeline:

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

const options = {
  taskName: "summarization",
  modelId: "Xenova/distilbart-cnn-6-6",
  modelHub: "huggingface"
};

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_new_tokens: 100 } };
const res = await engine.run(request);
console.log(res[0]["summary_text"]);

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, _config)

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.

  • _config (object) – The config

Returns:

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