Instructions to use PSW/summ-grounded-method-tweetsumm-reverse-trained-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PSW/summ-grounded-method-tweetsumm-reverse-trained-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PSW/summ-grounded-method-tweetsumm-reverse-trained-model") model = AutoModelForSeq2SeqLM.from_pretrained("PSW/summ-grounded-method-tweetsumm-reverse-trained-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from PSW/summ-grounded-method-tweetsumm-reverse-trained-model: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/PSW/summ-grounded-method-tweetsumm-reverse-trained-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://PSW/summ-grounded-method-tweetsumm-reverse-trained-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PSW/summ-grounded-method-tweetsumm-reverse-trained-model/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- 1495f3a7f583d5c1eec3ee335420fe5b01f0260dacca766b28bdca956c233d72
- Size of remote file:
- 558 MB
- SHA256:
- a0efc62161735b9611bd542641cfa76acdbd8e52338883d063133d39cdf5a9c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.