Instructions to use vuiseng9/bert-base-squadv1-block-pruning-hybrid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuiseng9/bert-base-squadv1-block-pruning-hybrid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vuiseng9/bert-base-squadv1-block-pruning-hybrid")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vuiseng9/bert-base-squadv1-block-pruning-hybrid") model = AutoModelForQuestionAnswering.from_pretrained("vuiseng9/bert-base-squadv1-block-pruning-hybrid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vuiseng9/bert-base-squadv1-block-pruning-hybrid: direct link, hf CLI and curl.
- Browser
- Download file 386 MB
-
https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vuiseng9/bert-base-squadv1-block-pruning-hybrid/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid/resolve/main/pytorch_model.bin
386 MB
- Xet hash:
- 2cf01fcb2aac12d6487e4c6b114c371263cc04179c3f270d7e7405079f44b083
- Size of remote file:
- 386 MB
- SHA256:
- 895559c3ab4ac710f1747e5a3d2b5a45fa34edc8975bd9843f3d5960588f05b6
路
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