Instructions to use CATIE-AQ/QAmembert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CATIE-AQ/QAmembert 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="CATIE-AQ/QAmembert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("CATIE-AQ/QAmembert") model = AutoModelForQuestionAnswering.from_pretrained("CATIE-AQ/QAmembert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from CATIE-AQ/QAmembert: direct link, hf CLI and curl.
- Browser
- Download file 811 kB
-
https://huggingface.co/CATIE-AQ/QAmembert/resolve/dd4f267d69ce996ee0e28df39bdc7c0bfa903aed/sentencepiece.bpe.model
- Command line
-
hf download hf://CATIE-AQ/QAmembert@dd4f267d69ce996ee0e28df39bdc7c0bfa903aed/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/CATIE-AQ/QAmembert/resolve/dd4f267d69ce996ee0e28df39bdc7c0bfa903aed/sentencepiece.bpe.model
811 kB
- Xet hash:
- 57d2beb07207a35b55bc6eedd16cd8c11992d807e423fa5a2fbc8eac5c7abde0
- Size of remote file:
- 811 kB
- SHA256:
- 988bc5a00281c6d210a5d34bd143d0363741a432fefe741bf71e61b1869d4314
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