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Include links to converted models

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Joshua Lochner 2023-02-23 01:35:04 +02:00
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@ -57,22 +57,23 @@ Check out our demo at [https://xenova.github.io/transformers.js/](https://xenova
### Convert PyTorch models to ONNX
We use [ONNX Runtime](https://onnxruntime.ai/) to run the models in the browser, so you must first convert your PyTorch model to ONNX (which can be done using our conversion script). For the following examples, we assume your PyTorch models are located in the ./models/pytorch/ folder. To choose a different location, specify the parent input folder with `--input_parent_dir /path/to/parent_dir/` (note: without the model id).
1. t5-small
Here are some of the models we have already converted (along with the command used).
1. [t5-small](https://huggingface.co/Xenova/t5-small_onnx-quantized) for translation/summarization.
```
python -m scripts.convert --quantize --model_id t5-small --task seq2seq-lm-with-past
```
2. gpt2
2. [distilgpt2](https://huggingface.co/Xenova/distilgpt2_onnx-quantized) for text generation.
```
python -m scripts.convert --quantize --model_id gpt2 --task causal-lm-with-past
python -m scripts.convert --quantize --model_id distilgpt2 --task causal-lm-with-past
```
3. bert-base-uncased
3. [bert-base-uncased](https://huggingface.co/Xenova/bert-base-uncased_onnx-quantized) for masked language modelling.
```
python -m scripts.convert --quantize --model_id bert-base-uncased --task masked-lm
```
4. bert-base-cased
4. [bert-base-cased](https://huggingface.co/Xenova/bert-base-cased_onnx-quantized) for masked language modelling.
```
python -m scripts.convert --quantize --model_id bert-base-cased --task masked-lm
```