Instructions to use tiagoblima/debug_t5-large_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiagoblima/debug_t5-large_squad with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tiagoblima/debug_t5-large_squad") model = AutoModelForSeq2SeqLM.from_pretrained("tiagoblima/debug_t5-large_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tiagoblima/debug_t5-large_squad: direct link, hf CLI and curl.
- Browser
- Download file 4.79 kB
-
https://huggingface.co/tiagoblima/debug_t5-large_squad/resolve/main/training_args.bin
- Command line
-
hf download hf://tiagoblima/debug_t5-large_squad/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tiagoblima/debug_t5-large_squad/resolve/main/training_args.bin
4.79 kB
- Xet hash:
- d4dd28fcc948726df12a7932dc72b05e2eb6d34f6531fd0ba209e66a5112e886
- Size of remote file:
- 4.79 kB
- SHA256:
- f66a19a38681e36cddd7304fe4d69ec4ac469ddaf89e6fffccfc98da1ee43117
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.