Instructions to use Aybars/ModelOnWhole with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aybars/ModelOnWhole 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="Aybars/ModelOnWhole")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Aybars/ModelOnWhole") model = AutoModelForQuestionAnswering.from_pretrained("Aybars/ModelOnWhole", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Aybars/ModelOnWhole: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/Aybars/ModelOnWhole/resolve/main/training_args.bin
- Command line
-
hf download hf://Aybars/ModelOnWhole/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Aybars/ModelOnWhole/resolve/main/training_args.bin
1.78 kB
- Xet hash:
- b0559232e52166209762a7d3a5507b64d42097c2d2605ede1c47ca01d90a3f20
- Size of remote file:
- 1.78 kB
- SHA256:
- 7c2851c9adfb1a444d06f9bb8ef3be38cdd75f13e12d3c7df551291870d15973
路
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