Instructions to use badalsahani/oneAPI_QA_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badalsahani/oneAPI_QA_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="badalsahani/oneAPI_QA_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("badalsahani/oneAPI_QA_Model") model = AutoModelForQuestionAnswering.from_pretrained("badalsahani/oneAPI_QA_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from badalsahani/oneAPI_QA_Model: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/badalsahani/oneAPI_QA_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://badalsahani/oneAPI_QA_Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/badalsahani/oneAPI_QA_Model/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 98f1e90d10acd0100847cfe1592579abb73a1182cc73f8b3adf416b9488d04e3
- Size of remote file:
- 4.54 kB
- SHA256:
- df5eb4dd4e6251528860634c474b1f837e7d1bcf769de030b1ad0f724e72c502
路
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