Instructions to use voidful/question-answering-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/question-answering-zh 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="voidful/question-answering-zh")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("voidful/question-answering-zh") model = AutoModelForQuestionAnswering.from_pretrained("voidful/question-answering-zh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from voidful/question-answering-zh: direct link, hf CLI and curl.
- Browser
- Download file 407 MB
-
https://huggingface.co/voidful/question-answering-zh/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://voidful/question-answering-zh/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/voidful/question-answering-zh/resolve/main/flax_model.msgpack
407 MB
- Xet hash:
- bfe68d63e56b0e91a8744df32128b78ce8b3c9e1ed227063ba12ed391858c40b
- Size of remote file:
- 407 MB
- SHA256:
- 0c28f188c89a954204dc0b2326aa0593d0947c6db03bd3e815ec42f608d2ac84
路
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