Instructions to use njt1980/JIRAHelper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use njt1980/JIRAHelper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="njt1980/JIRAHelper")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("njt1980/JIRAHelper") model = AutoModelForTableQuestionAnswering.from_pretrained("njt1980/JIRAHelper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from njt1980/JIRAHelper: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/njt1980/JIRAHelper/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://njt1980/JIRAHelper/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/njt1980/JIRAHelper/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 5f2ad3a5cc631fcbbca6f75cef74e145454d6014293726ae77facdadb958257a
- Size of remote file:
- 443 MB
- SHA256:
- d92be1b6ae75e6ca5c66192f367df5124aa24c4cc3d1c4e5b5299419b18af30e
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