Instructions to use ratishsp/Centrum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratishsp/Centrum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ratishsp/Centrum") model = AutoModelForSeq2SeqLM.from_pretrained("ratishsp/Centrum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ratishsp/Centrum: direct link, hf CLI and curl.
- Browser
- Download file 610 MB
-
https://huggingface.co/ratishsp/Centrum/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ratishsp/Centrum@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ratishsp/Centrum/resolve/refs%2Fpr%2F1/pytorch_model.bin
610 MB
- Xet hash:
- e01d25cfb47f6c97800ff3096a07a4baac45b2ceec7662eda43c4ba1e822866c
- Size of remote file:
- 610 MB
- SHA256:
- 6f32eb82307718433cdc1e4683b80a996130bc37ad24e5c459f1b503aee8ec9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.