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 pytorch_model.bin from bhumikak/resultsd: direct link, hf CLI and curl.
- Browser
- Download file 2.28 GB
-
https://huggingface.co/bhumikak/resultsd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bhumikak/resultsd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bhumikak/resultsd/resolve/main/pytorch_model.bin
2.28 GB
- Xet hash:
- 55beb73b3d4c9f7332fc76416050d110ee892ba4b943aab4be2b9becfff2f1b0
- Size of remote file:
- 2.28 GB
- SHA256:
- 376a5460b629c4efa68d5fe6bfe652168a1998d678990e8926fc26f045682a55
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