Instructions to use lysandre/tapas-temporary-repo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lysandre/tapas-temporary-repo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="lysandre/tapas-temporary-repo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("lysandre/tapas-temporary-repo") model = AutoModelForTableQuestionAnswering.from_pretrained("lysandre/tapas-temporary-repo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lysandre/tapas-temporary-repo: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/lysandre/tapas-temporary-repo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lysandre/tapas-temporary-repo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lysandre/tapas-temporary-repo/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 6e9f362c8251ff90177e1571db7c677734a88adb9f6a525a0a275078afd15d1a
- Size of remote file:
- 443 MB
- SHA256:
- 83911bd9bb4bf2fb9280a6fb1dc68eeaeb619127c3cf0cdcc7f8d9704055dbb8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.