Instructions to use vasa-fr/qna_syntec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasa-fr/qna_syntec 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="vasa-fr/qna_syntec")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vasa-fr/qna_syntec") model = AutoModelForQuestionAnswering.from_pretrained("vasa-fr/qna_syntec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vasa-fr/qna_syntec: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/vasa-fr/qna_syntec/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vasa-fr/qna_syntec/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vasa-fr/qna_syntec/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- ed38847ca564dc3b858d275f39be1ed34c30d1fd92cda14e3882f3fe2d8d6094
- Size of remote file:
- 440 MB
- SHA256:
- 4011851c2c0444049c712136bdc70267e6fb108ac3ffa2642ff7d9fe0269d76d
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