Instructions to use Aktsvigun/bart-base_xsum_919213 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aktsvigun/bart-base_xsum_919213 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Aktsvigun/bart-base_xsum_919213") model = AutoModelForSeq2SeqLM.from_pretrained("Aktsvigun/bart-base_xsum_919213", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Aktsvigun/bart-base_xsum_919213: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/Aktsvigun/bart-base_xsum_919213/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Aktsvigun/bart-base_xsum_919213/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Aktsvigun/bart-base_xsum_919213/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- 01df9eadb5835ccf78acac2ac958d62c6c6802f1798d75c70055bfe87ba516f3
- Size of remote file:
- 558 MB
- SHA256:
- ee292d2be4f4fde5a5bcee4cc704d39328f5f319776716ded87add35e5f9cd21
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.