Instructions to use allegro/herbert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allegro/herbert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="allegro/herbert-base-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-base-cased") model = AutoModel.from_pretrained("allegro/herbert-base-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from allegro/herbert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 654 MB
-
https://huggingface.co/allegro/herbert-base-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://allegro/herbert-base-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/allegro/herbert-base-cased/resolve/main/pytorch_model.bin
654 MB
- Xet hash:
- 0c1d84a637f8ddb5956d73e653202f2e553b59eef873160668b2d1134dd38fe6
- Size of remote file:
- 654 MB
- SHA256:
- d205995436644f5974156fad33ef9dd55d98bb7484918baa65e0fb62b6964a29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.