Instructions to use longcld/t5_small_checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use longcld/t5_small_checkpoint with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("longcld/t5_small_checkpoint") model = AutoModelForSeq2SeqLM.from_pretrained("longcld/t5_small_checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from longcld/t5_small_checkpoint: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/longcld/t5_small_checkpoint/resolve/main/training_args.bin
- Command line
-
hf download hf://longcld/t5_small_checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/longcld/t5_small_checkpoint/resolve/main/training_args.bin
1.33 kB
- Xet hash:
- 2efe4f2e836ab6987b6e743171b3d6b0c30219ac23414794c8523d0c9703d75b
- Size of remote file:
- 1.33 kB
- SHA256:
- 509b3bd1048bd48620a724cb465985e0fc6b20c90d0b89c6bd3682c6669b3559
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.