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 `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)
### ✅ Based on `decoder_model_merged.onnx` *with* slimming
↳ ✅ `fp16`: `decoder_model_merged_fp16.onnx` (added)
↳ ✅ `int8`: `decoder_model_merged_int8.onnx` (added)
↳ ✅ `uint8`: `decoder_model_merged_uint8.onnx` (added)
↳ ✅ `q4`: `decoder_model_merged_q4.onnx` (added)
↳ ✅ `q4f16`: `decoder_model_merged_q4f16.onnx` (added)
↳ ✅ `bnb4`: `decoder_model_merged_bnb4.onnx` (added)
### ✅ Based on `decoder_model_merged.onnx` *with* slimming
↳ ✅ `fp16`: `decoder_model_merged_fp16.onnx` (added)
↳ ✅ `int8`: `decoder_model_merged_int8.onnx` (added)
↳ ✅ `uint8`: `decoder_model_merged_uint8.onnx` (added)
↳ ✅ `q4`: `decoder_model_merged_q4.onnx` (added)
↳ ✅ `q4f16`: `decoder_model_merged_q4f16.onnx` (added)
↳ ✅ `bnb4`: `decoder_model_merged_bnb4.onnx` (added)
- 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_merged_bnb4.onnx +3 -0
- onnx/decoder_model_merged_fp16.onnx +3 -0
- onnx/decoder_model_merged_int8.onnx +3 -0
- onnx/decoder_model_merged_q4.onnx +3 -0
- onnx/decoder_model_merged_q4f16.onnx +3 -0
- onnx/decoder_model_merged_uint8.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
| @@ -5,4 +5,20 @@ library_name: transformers.js | |
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            https://huggingface.co/EleutherAI/pythia-70m 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/EleutherAI/pythia-70m 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/pythia-70m');
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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 [🤗 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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