Instructions to use ybelkada/tiny-mobilebertmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ybelkada/tiny-mobilebertmodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ybelkada/tiny-mobilebertmodel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ybelkada/tiny-mobilebertmodel") model = AutoModel.from_pretrained("ybelkada/tiny-mobilebertmodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ybelkada/tiny-mobilebertmodel: direct link, hf CLI and curl.
- Browser
- Download file 19.2 MB
-
https://huggingface.co/ybelkada/tiny-mobilebertmodel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ybelkada/tiny-mobilebertmodel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ybelkada/tiny-mobilebertmodel/resolve/main/pytorch_model.bin
19.2 MB
- Xet hash:
- cfde79b178a618da7b7d28085bda5bbadd397b6ab88b5421a60433341ae54f1f
- Size of remote file:
- 19.2 MB
- SHA256:
- 1bd757355a6a663da78ee880cd9f38249408a6feb7242a1cb1513bff1e7f82fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.