Instructions to use sunitha/FT_AQG_Configs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sunitha/FT_AQG_Configs 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="sunitha/FT_AQG_Configs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sunitha/FT_AQG_Configs") model = AutoModelForQuestionAnswering.from_pretrained("sunitha/FT_AQG_Configs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sunitha/FT_AQG_Configs: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/sunitha/FT_AQG_Configs/resolve/main/training_args.bin
- Command line
-
hf download hf://sunitha/FT_AQG_Configs/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sunitha/FT_AQG_Configs/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- becc383f8ef7a00b4e3cd3be48a54ab9e928409be5f869ee06a93b8f683ea64a
- Size of remote file:
- 3.06 kB
- SHA256:
- d04f2a9de04c6f92bb780d774928a860a67fbbfec6bd4fcd6fc8b1cbfa570891
路
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