Instructions to use ejschwartz/BinT5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ejschwartz/BinT5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ejschwartz/BinT5") model = AutoModelForSeq2SeqLM.from_pretrained("ejschwartz/BinT5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.4551605
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Rename README.md to README.md.codet5
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Adding `safetensors` variant of this model (#1)
- Update README.md (c3655a713782d44ed68c0ceedd6db2d9bd0078cc) - Adding `safetensors` variant of this model (553d094b35f216a895e9860c15540d2bfb052d9a) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>aff8b8d
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Add Bin T5 model
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Add Bin T5 model
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