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 model.safetensors from paulh27/xsum_aligned_smallT5_cont3: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/paulh27/xsum_aligned_smallT5_cont3/resolve/c60544fcbf2cd868014da28481baab5cf3ee1713/model.safetensors
- Command line
-
hf download hf://paulh27/xsum_aligned_smallT5_cont3@c60544fcbf2cd868014da28481baab5cf3ee1713/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/paulh27/xsum_aligned_smallT5_cont3/resolve/c60544fcbf2cd868014da28481baab5cf3ee1713/model.safetensors
242 MB
- Xet hash:
- 64ac528c5c8b8cbc1574658e5abca95b24265c97bec64bfd0abd31151c912485
- Size of remote file:
- 242 MB
- SHA256:
- 612167491228be146b637a7b66eb96e4953904b8f11d6cae1da038fe169efc92
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