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# Ignore artifacts: | ||
.github | ||
dist | ||
docs | ||
examples | ||
scripts | ||
types | ||
*.md |
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{ | ||
"overrides": [ | ||
{ | ||
"files": ["tests/**/*.js"], | ||
"options": { | ||
"printWidth": 10000000 | ||
} | ||
} | ||
] | ||
} |
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To install via [NPM](https://www.npmjs.com/package/@xenova/transformers), run: | ||
To install via [NPM](https://www.npmjs.com/package/@huggingface/transformers), run: | ||
```bash | ||
npm i @xenova/transformers | ||
npm i @huggingface/transformers | ||
``` | ||
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Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with: | ||
```html | ||
<script type="module"> | ||
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.17.2'; | ||
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.0.0'; | ||
</script> | ||
``` |
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By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@xenova/[email protected]/dist/), which should work out-of-the-box. You can customize this as follows: | ||
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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/[email protected]/dist/), which should work out-of-the-box. You can customize this as follows: | ||
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### Settings | ||
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```javascript | ||
import { env } from '@xenova/transformers'; | ||
import { env } from '@huggingface/transformers'; | ||
// Specify a custom location for models (defaults to '/models/'). | ||
env.localModelPath = '/path/to/models/'; | ||
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### Convert your models to ONNX | ||
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We recommend using our [conversion script](https://github.com/xenova/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. | ||
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. | ||
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```bash | ||
python -m scripts.convert --quantize --model_id <model_name_or_path> | ||
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