Instructions to use RitishK/qa-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RitishK/qa-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RitishK/qa-model") model = AutoModelForSeq2SeqLM.from_pretrained("RitishK/qa-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RitishK/qa-model: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/RitishK/qa-model/resolve/main/training_args.bin
- Command line
-
hf download hf://RitishK/qa-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RitishK/qa-model/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- ec9cabbd2359b18f8a8d32bd4129bf0e4b34f4aab694f798a096ee75a9cd9e02
- Size of remote file:
- 5.91 kB
- SHA256:
- 028c0c17b7443b1fb0bc28caaff22cb436c796a0dbdb7ac1c617c3f914fffcaf
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