Instructions to use francoMG/sara-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use francoMG/sara-qa 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="francoMG/sara-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("francoMG/sara-qa") model = AutoModelForQuestionAnswering.from_pretrained("francoMG/sara-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from francoMG/sara-qa: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/francoMG/sara-qa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://francoMG/sara-qa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/francoMG/sara-qa/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 537e04b6bbf1f7d1b078f0392ac5d19e144de0aeb94c2b6761fc3ce89a945e6f
- Size of remote file:
- 437 MB
- SHA256:
- 0e1969327bcd20cc0162fd8ff9bb4f4829cb3fb3d0cb30f1666dcc8d9e17ba94
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.