Instructions to use Aybars/ModelOnTquad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aybars/ModelOnTquad with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" 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("question-answering", model="Aybars/ModelOnTquad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Aybars/ModelOnTquad") model = AutoModelForQuestionAnswering.from_pretrained("Aybars/ModelOnTquad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Aybars/ModelOnTquad: direct link, hf CLI and curl.
- Browser
- Download file 735 MB
-
https://huggingface.co/Aybars/ModelOnTquad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Aybars/ModelOnTquad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Aybars/ModelOnTquad/resolve/main/pytorch_model.bin
735 MB
- Xet hash:
- 0c903a3a4f11755eab1fd30ff1520f0a0be1f2d6250dd4967ad586e72c47860c
- Size of remote file:
- 735 MB
- SHA256:
- ef963cdfb91479fdb5a0e9e99c700918e8ed418334c8765e36d23bccf67bbbc6
路
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