Instructions to use Yannis98/tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yannis98/tmp 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="Yannis98/tmp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Yannis98/tmp") model = AutoModelForQuestionAnswering.from_pretrained("Yannis98/tmp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download full_predict_tr_results.json from Yannis98/tmp: direct link, hf CLI and curl.
- Browser
- Download file 15.6 MB
-
https://huggingface.co/Yannis98/tmp/resolve/main/full_predict_tr_results.json
- Command line
-
hf download hf://Yannis98/tmp/full_predict_tr_results.json
-
curl -L -o full_predict_tr_results.json https://huggingface.co/Yannis98/tmp/resolve/main/full_predict_tr_results.json
15.6 MB
- Xet hash:
- b54c2f42205c48f0b9351d0431d027b6adeec2e9d79bf17c22c5a4a7adcb1a55
- Size of remote file:
- 15.6 MB
- SHA256:
- f2d2ab3b08be3c34eee6a0633e9075ab9cc863dc868e1ba3d8d6ec5d5f45d037
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.