Instructions to use voidful/bart-qg-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/bart-qg-zh with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("voidful/bart-qg-zh") model = AutoModelForSeq2SeqLM.from_pretrained("voidful/bart-qg-zh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from voidful/bart-qg-zh: direct link, hf CLI and curl.
- Browser
- Download file 719 MB
-
https://huggingface.co/voidful/bart-qg-zh/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://voidful/bart-qg-zh/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/voidful/bart-qg-zh/resolve/main/pytorch_model.bin
719 MB
- Xet hash:
- 37176c8e103a8bc2552efbc49c986f4957f168e0957f18a6c3a3b68efbe8e482
- Size of remote file:
- 719 MB
- SHA256:
- 3dab3c606a007d0eeaf6742c147bc3f9f115fafb002a0dec8234dd0ab429b7e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.