Instructions to use santyzenith/train_t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use santyzenith/train_t5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("santyzenith/train_t5") model = AutoModelForSeq2SeqLM.from_pretrained("santyzenith/train_t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from santyzenith/train_t5: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/santyzenith/train_t5/resolve/main/training_args.bin
- Command line
-
hf download hf://santyzenith/train_t5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/santyzenith/train_t5/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- c0fb9feb0647feaec8489c1890a4a67559ee3cb4d3046a471cdb46b80b60dada
- Size of remote file:
- 4.22 kB
- SHA256:
- 0556d5551b42e83aa09d4217734e0aa6435ce3cb34ac5d2deae632b0a301afc4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.