Instructions to use Artix1806/test_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Artix1806/test_rag_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="Artix1806/test_rag_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Artix1806/test_rag_model") model = AutoModelForQuestionAnswering.from_pretrained("Artix1806/test_rag_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Artix1806/test_rag_model: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/Artix1806/test_rag_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Artix1806/test_rag_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Artix1806/test_rag_model/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 81fabdff7e86c8609a8bb3faae1dfc3fe8293ac62e24105ebaf003b6054524aa
- Size of remote file:
- 5.18 kB
- SHA256:
- b8cab723cae6b0892cd50ccae7ede79c0f60a333320a63c5125f4907e9af4b44
路
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