Instructions to use echarlaix/t5-small-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use echarlaix/t5-small-onnx with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="echarlaix/t5-small-onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("echarlaix/t5-small-onnx") model = AutoModelForSeq2SeqLM.from_pretrained("echarlaix/t5-small-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_model.onnx from echarlaix/t5-small-onnx: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/echarlaix/t5-small-onnx/resolve/main/decoder_model.onnx
- Command line
-
hf download hf://echarlaix/t5-small-onnx/decoder_model.onnx
-
curl -L -o decoder_model.onnx https://huggingface.co/echarlaix/t5-small-onnx/resolve/main/decoder_model.onnx
167 MB
- Xet hash:
- 76e850f355cd53cdeeeec9dc24d0250d0b2e16a5cd516aa02d479360642c56c5
- Size of remote file:
- 167 MB
- SHA256:
- afdf24ba2853b77665cabc3a9f0687a81d96b836620eeb09eabf0b748b1aaf43
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