Instructions to use Linggg/t5_summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linggg/t5_summary with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Linggg/t5_summary") model = AutoModelForSeq2SeqLM.from_pretrained("Linggg/t5_summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Linggg/t5_summary: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/Linggg/t5_summary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Linggg/t5_summary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Linggg/t5_summary/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 47ceb81a8c67fb8c0a63b8c5e513c530b6b4042158189e795f07886490ada5b5
- Size of remote file:
- 1.2 GB
- SHA256:
- 26502ce822828fda8af3467e8437c8448531876de5062e55172eb715194f506e
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