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 pytorch_model.bin from mateiaass/albertbase-qa_2: direct link, hf CLI and curl.
- Browser
- Download file 44.4 MB
-
https://huggingface.co/mateiaass/albertbase-qa_2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mateiaass/albertbase-qa_2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mateiaass/albertbase-qa_2/resolve/main/pytorch_model.bin
44.4 MB
- Xet hash:
- 14965a1b290670aec9ac87874e389973a49e48dd833dd6ebdd91824e7cd483d3
- Size of remote file:
- 44.4 MB
- SHA256:
- e6a147112d657916151425680daf673ae89118e3f5adbf96c9763806bdd779b3
路
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