Instructions to use tiagoblima/debug_t5-base_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiagoblima/debug_t5-base_squad with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tiagoblima/debug_t5-base_squad") model = AutoModelForSeq2SeqLM.from_pretrained("tiagoblima/debug_t5-base_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tiagoblima/debug_t5-base_squad: direct link, hf CLI and curl.
- Browser
- Download file 4.79 kB
-
https://huggingface.co/tiagoblima/debug_t5-base_squad/resolve/main/training_args.bin
- Command line
-
hf download hf://tiagoblima/debug_t5-base_squad/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tiagoblima/debug_t5-base_squad/resolve/main/training_args.bin
4.79 kB
- Xet hash:
- 51848e68be804bdc84de5c3b04a4874cf3192497124e11c8fa857829c069b327
- Size of remote file:
- 4.79 kB
- SHA256:
- 6cdaaea37d415b095192694cf13561001a0e8093a8af11581d233f141d318e1e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.