Instructions to use vasumathin298/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasumathin298/model 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="vasumathin298/model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vasumathin298/model") model = AutoModelForQuestionAnswering.from_pretrained("vasumathin298/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vasumathin298/model: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/vasumathin298/model/resolve/main/training_args.bin
- Command line
-
hf download hf://vasumathin298/model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vasumathin298/model/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- db6505531f74995c0d687b20ed935b594c3bc83e51e5da417d718ac9efb709bd
- Size of remote file:
- 5.18 kB
- SHA256:
- c95462e9f0d55ad6b32d7051cfa8fede23865cfc839b2c06e25f1371573eb7e2
路
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