transformers.js/pr_1480/en/custom_usage.md

# Use custom models

By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.8.1/dist/), which should work out-of-the-box. You can customize this as follows:

### Settings

```javascript
import { env } from '@huggingface/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](./api/env).

### Convert your models to ONNX

We recommend using our [conversion script](https://github.com/huggingface/transformers.js/blob/main/scripts/convert.py) to convert your PyTorch, TensorFlow, or JAX models to ONNX in a single command. Behind the scenes, it uses [🤗 Optimum](https://huggingface.co/docs/optimum) to perform conversion and quantization of your model.

```bash
python -m scripts.convert --quantize --model_id 
```

For example, convert and quantize [bert-base-uncased](https://huggingface.co/bert-base-uncased) using:
```bash
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
```

For the full list of supported architectures, see the [Optimum documentation](https://huggingface.co/docs/optimum/main/en/exporters/onnx/overview).