Instructions to use Patcas/my_awesome-assert-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/my_awesome-assert-new with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patcas/my_awesome-assert-new") model = AutoModelForSeq2SeqLM.from_pretrained("Patcas/my_awesome-assert-new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Patcas/my_awesome-assert-new: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/Patcas/my_awesome-assert-new/resolve/main/training_args.bin
- Command line
-
hf download hf://Patcas/my_awesome-assert-new/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Patcas/my_awesome-assert-new/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- e658a1346f064813ac643325d35b7ffb51e9baffbabf7bb50a7c5859bfc19a89
- Size of remote file:
- 4.73 kB
- SHA256:
- 0c1e976db9fff96eb513c6b38e5e6890107aec0d63a7a0c285823c8306e84cb4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.