Instructions to use sv-adv/qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sv-adv/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="sv-adv/qa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sv-adv/qa_model") model = AutoModelForQuestionAnswering.from_pretrained("sv-adv/qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-500/optimizer.pt from sv-adv/qa_model: direct link, hf CLI and curl.
- Browser
- Download file 1 GB
-
https://huggingface.co/sv-adv/qa_model/resolve/main/checkpoint-500/optimizer.pt
- Command line
-
hf download hf://sv-adv/qa_model/checkpoint-500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/sv-adv/qa_model/resolve/main/checkpoint-500/optimizer.pt
1 GB
- Xet hash:
- 48881b50b87b5907fb7b07a2742b91260770053da360673f8c5429865ee63ace
- Size of remote file:
- 1 GB
- SHA256:
- 064fea183fd94d98b964791cdfab80e6f6deca96bea288fc9e1bea5cf3d578c1
路
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