Add/update the quantized ONNX model files and README.md for Transformers.js v3
Browse files## Applied Quantizations
### ✅ Based on `decoder_model.onnx` *with* slimming
↳ ✅ `fp16`: `decoder_model_fp16.onnx` (added)
↳ ✅ `int8`: `decoder_model_int8.onnx` (added)
↳ ✅ `uint8`: `decoder_model_uint8.onnx` (added)
↳ ✅ `q4`: `decoder_model_q4.onnx` (added)
↳ ✅ `q4f16`: `decoder_model_q4f16.onnx` (added)
↳ ✅ `bnb4`: `decoder_model_bnb4.onnx` (added)
### ✅ Based on `decoder_model.onnx` *with* slimming
↳ ✅ `fp16`: `decoder_model_fp16.onnx` (added)
↳ ✅ `int8`: `decoder_model_int8.onnx` (added)
↳ ✅ `uint8`: `decoder_model_uint8.onnx` (added)
↳ ✅ `q4`: `decoder_model_q4.onnx` (added)
↳ ✅ `q4f16`: `decoder_model_q4f16.onnx` (added)
↳ ✅ `bnb4`: `decoder_model_bnb4.onnx` (added)
### ✅ Based on `encoder_model.onnx` *with* slimming
↳ ✅ `int8`: `encoder_model_int8.onnx` (added)
↳ ✅ `uint8`: `encoder_model_uint8.onnx` (added)
↳ ✅ `q4`: `encoder_model_q4.onnx` (added)
↳ ✅ `q4f16`: `encoder_model_q4f16.onnx` (added)
↳ ✅ `bnb4`: `encoder_model_bnb4.onnx` (added)
### ✅ Based on `encoder_model.onnx` *with* slimming
↳ ✅ `int8`: `encoder_model_int8.onnx` (added)
↳ ✅ `uint8`: `encoder_model_uint8.onnx` (added)
↳ ✅ `q4`: `encoder_model_q4.onnx` (added)
↳ ✅ `q4f16`: `encoder_model_q4f16.onnx` (added)
↳ ✅ `bnb4`: `encoder_model_bnb4.onnx` (added)
### ✅ Based on `decoder_with_past_model.onnx` *with* slimming
↳ ✅ `fp16`: `decoder_with_past_model_fp16.onnx` (added)
↳ ✅ `int8`: `decoder_with_past_model_int8.onnx` (added)
↳ ✅ `uint8`: `decoder_with_past_model_uint8.onnx` (added)
↳ ✅ `q4`: `decoder_with_past_model_q4.onnx` (added)
↳ ✅ `q4f16`: `decoder_with_past_model_q4f16.onnx` (added)
↳ ✅ `bnb4`: `decoder_with_past_model_bnb4.onnx` (added)
### ✅ Based on `decoder_with_past_model.onnx` *with* slimming
↳ ✅ `fp16`: `decoder_with_past_model_fp16.onnx` (added)
↳ ✅ `int8`: `decoder_with_past_model_int8.onnx` (added)
↳ ✅ `uint8`: `decoder_with_past_model_uint8.onnx` (added)
↳ ✅ `q4`: `decoder_with_past_model_q4.onnx` (added)
↳ ✅ `q4f16`: `decoder_with_past_model_q4f16.onnx` (added)
↳ ✅ `bnb4`: `decoder_with_past_model_bnb4.onnx` (added)
- README.md +18 -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/encoder_model_bnb4.onnx +3 -0
- onnx/encoder_model_int8.onnx +3 -0
- onnx/encoder_model_q4.onnx +3 -0
- onnx/encoder_model_q4f16.onnx +3 -0
- onnx/encoder_model_uint8.onnx +3 -0
@@ -5,4 +5,22 @@ library_name: transformers.js
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https://huggingface.co/google/flan-t5-small 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 [🤗 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/google/flan-t5-small 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-to-text generation.
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```js
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import { pipeline } from '@huggingface/transformers';
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const generator = await pipeline('text2text-generation', 'Xenova/flan-t5-small');
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const output = await generator('how can I become more healthy?', {
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max_new_tokens: 100,
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});
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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 [🤗 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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