Instructions to use Ekata/mcqa-dpo-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ekata/mcqa-dpo-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ekata/mcqa-dpo-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ekata/mcqa-dpo-v1: direct link, hf CLI and curl.
- Browser
- Download file 6.14 kB
-
https://huggingface.co/Ekata/mcqa-dpo-v1/resolve/main/training_args.bin
- Command line
-
hf download hf://Ekata/mcqa-dpo-v1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ekata/mcqa-dpo-v1/resolve/main/training_args.bin
6.14 kB
- Xet hash:
- b88bab9730032aa9d50791884b2bc436abafbad794a026cc010c1b96dee77b5a
- Size of remote file:
- 6.14 kB
- SHA256:
- 9ef302d876b9ca172494f6424226c80a913488a7056ff6f499033740e78ee972
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