Instructions to use RyanHaniff/concordia_trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RyanHaniff/concordia_trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="RyanHaniff/concordia_trained_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("RyanHaniff/concordia_trained_model") model = AutoModelForTableQuestionAnswering.from_pretrained("RyanHaniff/concordia_trained_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RyanHaniff/concordia_trained_model: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/RyanHaniff/concordia_trained_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RyanHaniff/concordia_trained_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RyanHaniff/concordia_trained_model/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 0ec0bd979a83c284e1d8556407247d4fc5ea2803939ef48eb7c4693974fc3e90
- Size of remote file:
- 443 MB
- SHA256:
- c4905598d4264f4ad2caf98effcc91b2cfebd5f586ced5740960752318f74cb4
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