Instructions to use mateiaass/albertbase-qa_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateiaass/albertbase-qa_2 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="mateiaass/albertbase-qa_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mateiaass/albertbase-qa_2") model = AutoModelForQuestionAnswering.from_pretrained("mateiaass/albertbase-qa_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mateiaass/albertbase-qa_2: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/mateiaass/albertbase-qa_2/resolve/main/training_args.bin
- Command line
-
hf download hf://mateiaass/albertbase-qa_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mateiaass/albertbase-qa_2/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- e9702bc63c396c1c0cf97172783693fc40db1388468035d201dd6c284b1687ef
- Size of remote file:
- 4.03 kB
- SHA256:
- c85ab3e90ac8d563196e4e0dd15c049852efb129a8223b904e6ad9cdc09b5262
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.