Instructions to use mateiaass/albertbase-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateiaass/albertbase-qa 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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mateiaass/albertbase-qa") model = AutoModelForQuestionAnswering.from_pretrained("mateiaass/albertbase-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mateiaass/albertbase-qa: direct link, hf CLI and curl.
- Browser
- Download file 44.4 MB
-
https://huggingface.co/mateiaass/albertbase-qa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mateiaass/albertbase-qa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mateiaass/albertbase-qa/resolve/main/pytorch_model.bin
44.4 MB
- Xet hash:
- 6aef27458566cfdbc359ea77481ef14e22a30940e6c1e1046fb0374f7834f817
- Size of remote file:
- 44.4 MB
- SHA256:
- 50dc0298760311bb413decf11b966487b20a275a440d915af9ea59bc55f34aaa
路
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