Instructions to use paulh27/xsum_aligned_smallT5_cont3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paulh27/xsum_aligned_smallT5_cont3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("paulh27/xsum_aligned_smallT5_cont3") model = AutoModelForSeq2SeqLM.from_pretrained("paulh27/xsum_aligned_smallT5_cont3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from paulh27/xsum_aligned_smallT5_cont3: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/paulh27/xsum_aligned_smallT5_cont3/resolve/c60544fcbf2cd868014da28481baab5cf3ee1713/training_args.bin
- Command line
-
hf download hf://paulh27/xsum_aligned_smallT5_cont3@c60544fcbf2cd868014da28481baab5cf3ee1713/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/paulh27/xsum_aligned_smallT5_cont3/resolve/c60544fcbf2cd868014da28481baab5cf3ee1713/training_args.bin
5.11 kB
- Xet hash:
- 052387f8aedf8d75a908e466c0f4a4fc0e7209cf0a8c2e2f4a164a5a3a0de5e8
- Size of remote file:
- 5.11 kB
- SHA256:
- 261390f23d7cd06eef83897b57a90c47983ad073029252832045f78003f5254f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.