Instructions to use abazoge/DrBERT-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abazoge/DrBERT-4096 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="abazoge/DrBERT-4096")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("abazoge/DrBERT-4096") model = AutoModelForMaskedLM.from_pretrained("abazoge/DrBERT-4096", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from abazoge/DrBERT-4096: direct link, hf CLI and curl.
- Browser
- Download file 539 MB
-
https://huggingface.co/abazoge/DrBERT-4096/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://abazoge/DrBERT-4096/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/abazoge/DrBERT-4096/resolve/main/pytorch_model.bin
539 MB
- Xet hash:
- ad05302fdc63de02dcd703ca910c29fdc5ca56384b6ddfeba5087c4c41848299
- Size of remote file:
- 539 MB
- SHA256:
- e225cbcadbbdc5ca2d19ec656133043dbc36c7e3bd14b4dafa01ab8b66092014
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.