#
Use custom models

By default, Transformers.js uses hosted pretrained models and precompiled WASM binaries, which should work out-of-the-box. You can customize this as follows:

###
Settings

    import { env } from '@xenova/transformers';

    // Specify a custom location for models (defaults to '/models/').
    env.localModelPath = '/path/to/models/';

    // Disable the loading of remote models from the Hugging Face Hub:
    env.allowRemoteModels = false;

    // Set location of .wasm files. Defaults to use a CDN.
    env.backends.onnx.wasm.wasmPaths = '/path/to/files/';

For a full list of available settings, check out the API Reference.

###
Convert your models to ONNX

We recommend using our conversion script to convert your PyTorch, TensorFlow, or JAX models to ONNX in a single command. Behind the scenes, it uses 🤗 Optimum to perform conversion and quantization of your model.

    python -m scripts.convert --quantize --model_id <model_name_or_path>

For example, convert and quantize bert-base-uncased using:

    python -m scripts.convert --quantize --model_id bert-base-uncased

This will save the following files to `./models/`:

    bert-base-uncased/
    ├── config.json
    ├── tokenizer.json
    ├── tokenizer_config.json
    └── onnx/
        ├── model.onnx
        └── model_quantized.onnx