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 pytorch_model.bin from badalsahani/oneAPI_QA_Model: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/badalsahani/oneAPI_QA_Model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://badalsahani/oneAPI_QA_Model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/badalsahani/oneAPI_QA_Model/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 5ac1d6c5d77f0c4fefb0b6fae502795716d4c8786fc8db6bb18048cdc31987a1
- Size of remote file:
- 265 MB
- SHA256:
- 38310f011da1d86909a0bf181585b7e6937689ec47aa3b6480c1e2b94e4c51ba
路
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