Instructions to use Devmapall/paraphrase-quora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devmapall/paraphrase-quora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Devmapall/paraphrase-quora") model = AutoModelForSeq2SeqLM.from_pretrained("Devmapall/paraphrase-quora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Devmapall/paraphrase-quora: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/Devmapall/paraphrase-quora/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Devmapall/paraphrase-quora/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Devmapall/paraphrase-quora/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- 1d1b772608a40b70e81b6e76134ef2edcc9d69d5940ede1b30aa4ef3c51134ad
- Size of remote file:
- 892 MB
- SHA256:
- 8411ea41e5f758750ece262eb3610fce67f2bf5ddcab60c74c9d2ca59d2874f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.