Instructions to use Vaibhavbrkn/t5-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vaibhavbrkn/t5-summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vaibhavbrkn/t5-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Vaibhavbrkn/t5-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Vaibhavbrkn/t5-summarization: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/Vaibhavbrkn/t5-summarization/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Vaibhavbrkn/t5-summarization/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Vaibhavbrkn/t5-summarization/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- 1406828e5238a7c97c3a4797da62aa8c11c6d7dbe43bf06f982ea1c23674b34c
- Size of remote file:
- 892 MB
- SHA256:
- 402f3dafc3afc028e962772082e570bce27814df135fe8f53772545c45262d2b
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