Instructions to use bhumikak/resultsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhumikak/resultsd with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bhumikak/resultsd") model = AutoModelForSeq2SeqLM.from_pretrained("bhumikak/resultsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bhumikak/resultsd: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/bhumikak/resultsd/resolve/main/training_args.bin
- Command line
-
hf download hf://bhumikak/resultsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bhumikak/resultsd/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 1102814126e2bf14f0ed5f6bf007972331528af8a88f4286b07c6c41c23ba283
- Size of remote file:
- 3.44 kB
- SHA256:
- 806ff6458e5625c8f563b326b5d1ccdbeb9576e6217864d5b2ff29c00da3fd6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.