Instructions to use jamesliounis/MeDistilBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamesliounis/MeDistilBERT with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, MeDistilBERT tokenizer = AutoTokenizer.from_pretrained("jamesliounis/MeDistilBERT") model = MeDistilBERT.from_pretrained("jamesliounis/MeDistilBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jamesliounis/MeDistilBERT: direct link, hf CLI and curl.
- Browser
- Download file 54 MB
-
https://huggingface.co/jamesliounis/MeDistilBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jamesliounis/MeDistilBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jamesliounis/MeDistilBERT/resolve/main/pytorch_model.bin
54 MB
- Xet hash:
- fcb0800ac93918b918731ee9450d0dde3bdcf8e8ff64afbe8855bc2099cb2868
- Size of remote file:
- 54 MB
- SHA256:
- 6531cdd55007ca2ccce497e90354589d8ab51145dab55f3397e42e929a369272
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.