Instructions to use Xenova/t5-small_onnx-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xenova/t5-small_onnx-quantized with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Xenova/t5-small_onnx-quantized") model = AutoModelForSeq2SeqLM.from_pretrained("Xenova/t5-small_onnx-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_model.onnx from Xenova/t5-small_onnx-quantized: direct link, hf CLI and curl.
- Browser
- Download file 58.8 MB
-
https://huggingface.co/Xenova/t5-small_onnx-quantized/resolve/main/decoder_model.onnx
- Command line
-
hf download hf://Xenova/t5-small_onnx-quantized/decoder_model.onnx
-
curl -L -o decoder_model.onnx https://huggingface.co/Xenova/t5-small_onnx-quantized/resolve/main/decoder_model.onnx
58.8 MB
- Xet hash:
- 68f57b466695f05c362b1156e3cfa0bd00b74440c76655fd059559a92f0f060b
- Size of remote file:
- 58.8 MB
- SHA256:
- 59aaf020f91d2875be2b6a302ada94922c1042166f97694b90ca6f41d52330b6
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