Instructions to use serhii-korobchenko/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use serhii-korobchenko/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="serhii-korobchenko/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("serhii-korobchenko/dummy-model") model = AutoModelForMaskedLM.from_pretrained("serhii-korobchenko/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from serhii-korobchenko/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 543 MB
-
https://huggingface.co/serhii-korobchenko/dummy-model/resolve/main/tf_model.h5
- Command line
-
hf download hf://serhii-korobchenko/dummy-model/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/serhii-korobchenko/dummy-model/resolve/main/tf_model.h5
543 MB
- Xet hash:
- ea2c3e1acf4006f7192eba85769419f90a04dc38455d3e136295cedb4ed76ca4
- Size of remote file:
- 543 MB
- SHA256:
- e9a18f2a78d38bbb7f8c1d70616f945c181fad6dcc28430c3fd7eec4cff00caf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.