Instructions to use rlimonta/qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rlimonta/qa_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="rlimonta/qa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("rlimonta/qa_model") model = AutoModelForQuestionAnswering.from_pretrained("rlimonta/qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rlimonta/qa_model: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/rlimonta/qa_model/resolve/main/training_args.bin
- Command line
-
hf download hf://rlimonta/qa_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rlimonta/qa_model/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- 73d2dedf8c3245f16fa5d3e2947f87b3bdf7b1fed95fa913111b676e59cefa2b
- Size of remote file:
- 5.84 kB
- SHA256:
- 2c8787a377bcc7f8a1b88a4378055ca5cd2b81e93555bb26e772e1231065516b
路
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