Instructions to use Cheng98/opt-125m-boolq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheng98/opt-125m-boolq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/opt-125m-boolq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheng98/opt-125m-boolq") model = AutoModelForSequenceClassification.from_pretrained("Cheng98/opt-125m-boolq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Cheng98/opt-125m-boolq: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/Cheng98/opt-125m-boolq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cheng98/opt-125m-boolq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cheng98/opt-125m-boolq/resolve/main/pytorch_model.bin
501 MB
- Xet hash:
- 91ad44aa94fa306c49d58d41f3c0d7dee0c716f7de564226587e9f57fa4a3ab9
- Size of remote file:
- 501 MB
- SHA256:
- 9873491ef883a28fc7b16c18de013bd4fe14db9305e43e77ac7314a74a28f27a
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