Instructions to use kbnlresearch/eng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbnlresearch/eng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kbnlresearch/eng")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kbnlresearch/eng") model = AutoModelForMaskedLM.from_pretrained("kbnlresearch/eng", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kbnlresearch/eng: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/kbnlresearch/eng/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kbnlresearch/eng/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kbnlresearch/eng/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 173b134c05188bfc885dc1a250224ba2d6053690c3960bf362c35d11dd52203a
- Size of remote file:
- 712 MB
- SHA256:
- 8f28263f35796d57c11ee66c93c2dd22ec66063112a82a70fc206bec91a323e2
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