Add/update the quantized ONNX model files and README.md for Transformers.js v3
#3
by
whitphx
HF Staff
- opened
- README.md +16 -0
- onnx/decoder_model_bnb4.onnx +3 -0
- onnx/decoder_model_fp16.onnx +3 -0
- onnx/decoder_model_int8.onnx +3 -0
- onnx/decoder_model_q4.onnx +3 -0
- onnx/decoder_model_q4f16.onnx +3 -0
- onnx/decoder_model_uint8.onnx +3 -0
- onnx/decoder_with_past_model_bnb4.onnx +3 -0
- onnx/decoder_with_past_model_fp16.onnx +3 -0
- onnx/decoder_with_past_model_int8.onnx +3 -0
- onnx/decoder_with_past_model_q4.onnx +3 -0
- onnx/decoder_with_past_model_q4f16.onnx +3 -0
- onnx/decoder_with_past_model_uint8.onnx +3 -0
- onnx/model_bnb4.onnx +3 -0
- onnx/model_fp16.onnx +3 -0
- onnx/model_int8.onnx +3 -0
- onnx/model_q4.onnx +3 -0
- onnx/model_q4f16.onnx +3 -0
- onnx/model_uint8.onnx +3 -0
README.md
CHANGED
@@ -5,4 +5,20 @@ library_name: transformers.js
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https://huggingface.co/bigscience/bloomz-560m with ONNX weights to be compatible with Transformers.js.
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Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [π€ Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
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https://huggingface.co/bigscience/bloomz-560m with ONNX weights to be compatible with Transformers.js.
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## Usage (Transformers.js)
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
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```bash
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npm i @huggingface/transformers
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```
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**Example:** Text generation.
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```js
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import { pipeline } from '@huggingface/transformers';
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const generator = await pipeline('text-generation', 'Xenova/bloomz-560m');
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const output = await generator('Once upon a time, there was', { max_new_tokens: 10 });
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```
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Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [π€ Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
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onnx/decoder_model_bnb4.onnx
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onnx/decoder_model_fp16.onnx
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onnx/decoder_model_int8.onnx
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onnx/decoder_model_q4f16.onnx
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onnx/decoder_with_past_model_bnb4.onnx
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onnx/decoder_with_past_model_int8.onnx
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