Instructions to use Schnitzl/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Schnitzl/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Schnitzl/dummy-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Schnitzl/dummy-model") model = AutoModelForMaskedLM.from_pretrained("Schnitzl/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Schnitzl/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 543 MB
-
https://huggingface.co/Schnitzl/dummy-model/resolve/main/tf_model.h5
- Command line
-
hf download hf://Schnitzl/dummy-model/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Schnitzl/dummy-model/resolve/main/tf_model.h5
543 MB
- Xet hash:
- 2a9b3ba06dede04f98ec9bd9199169774574f06f5c5517087d6c703ce00e70b5
- Size of remote file:
- 543 MB
- SHA256:
- a5455e30517acc416200510bcf8d58d2a767b8d5f122cd3eb17764668673947e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.