Instructions to use Ulangi/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ulangi/checkpoints 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="Ulangi/checkpoints")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Ulangi/checkpoints") model = AutoModelForQuestionAnswering.from_pretrained("Ulangi/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ulangi/checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Ulangi/checkpoints/resolve/main/training_args.bin
- Command line
-
hf download hf://Ulangi/checkpoints/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ulangi/checkpoints/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- e7f79c15460676c1583726667b82a91d34a41d05c903bf5706019fa969e9b7a0
- Size of remote file:
- 5.3 kB
- SHA256:
- 63a811963f9bbdce2b3942d4f642f1eba32b4244ea6649cb84e7d2f92801b3ee
路
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